3,151 research outputs found
An Automated System for Stock Market Trading Based on Logical Clustering
In this paper a novel clustering-based system for automated stock market trading is introduced. It relies on interpolative Boolean algebra as underlying mathematical framework used to construct logical clustering method which is the central component of the system. The system uses fundamental analysis ratios, more precisely market valuation ratios, as clustering variables to differentiate between undervaluated and overvaluated stocks. To structure investment portfolio, the proposed system uses special weighting formulas which automatically diversify investment funds. Finally, a simple trading simulation engine is developed to test our system on real market data. The proposed system was tested on Belgrade Stock Exchange historical data and was able to achieve a high rate of return and to outperform the BelexLine market index as a benchmark variable. The paper has also provided in-depth analysis of the systemâs investment decision making process which reveals some exciting insights
Fuzzy Natural Logic in IFSA-EUSFLAT 2021
The present book contains five papers accepted and published in the Special Issue, âFuzzy Natural Logic in IFSA-EUSFLAT 2021â, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference âThe 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferencesâ, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IFâTHEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications
Neuro-Fuzzy Based Intelligent Approaches to Nonlinear System Identification and Forecasting
Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function.
Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the TakagiâSugenoâKang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the âcurse of dimensionalityâ problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study.
In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural
Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure,
incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beefâs temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the
same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric
function acting as input linguistic node. Since the asymmetric Gaussian membership functionâs variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINNâs MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems
Multi-agent system for flood forecasting in Tropical River Basin
It is well known, the problems related to the generation of floods, their control, and management,
have been treated with traditional hydrologic modeling tools focused on the study and
the analysis of the precipitation-runoff relationship, a physical process which is driven by the
hydrological cycle and the climate regime and that is directly proportional to the generation
of floodwaters. Within the hydrological discipline, they classify these traditional modeling
tools according to three principal groups, being the first group defined as trial-and-error models
(e.g., "black-models"), the second group are the conceptual models, which are categorized
in three main sub-groups as "lumped", "semi-lumped" and "semi-distributed", according to
the special distribution, and finally, models that are based on physical processes, known as
"white-box models" are the so-called "distributed-models". On the other hand, in engineering
applications, there are two types of models used in streamflow forecasting, and which are
classified concerning the type of measurements and variables required as "physically based
models", as well as "data-driven models".
The Physically oriented prototypes present an in-depth account of the dynamics related
to the physical aspects that occur internally among the different systems of a given hydrographic
basin. However, aside from being laborious to implement, they rely thoroughly
on mathematical algorithms, and an understanding of these interactions requires the abstraction
of mathematical concepts and the conceptualization of the physical processes that
are intertwined among these systems. Besides, models determined by data necessitates an
a-priori understanding of the physical laws controlling the process within the system, and
they are bound to mathematical formulations, which require a lot of numeric information
for field adjustments. Therefore, these models are remarkably different from each other
because of their needs for data, and their interpretation of physical phenomena. Although
there is considerable progress in hydrologic modeling for flood forecasting, several significant
setbacks remain unresolved, given the stochastic nature of the hydrological phenomena, is
the challenge to implement user-friendly, re-usable, robust, and reliable forecasting systems,
the amount of uncertainty they must deal with when trying to solve the flood forecasting
problem. However, in the past decades, with the growing environment and development of
the artificial intelligence (AI) field, some researchers have seldomly attempted to deal with
the stochastic nature of hydrologic events with the application of some of these techniques.
Given the setbacks to hydrologic flood forecasting previously described this thesis research
aims to integrate the physics-based hydrologic, hydraulic, and data-driven models under the
paradigm of Multi-agent Systems for flood forecasting by designing and developing a multi-agent system (MAS) framework for flood forecasting events within the scope of tropical
watersheds.
With the emergence of the agent technologies, the "agent-based modeling" and "multiagent
systems" simulation methods have provided applications for some areas of hydro base
management like flood protection, planning, control, management, mitigation, and forecasting
to combat the shocks produced by floods on society; however, all these focused on
evacuation drills, and the latter not aimed at the tropical river basin, whose hydrological
regime is extremely unique.
In this catchment modeling environment approach, it was applied the multi-agent systems
approach as a surrogate of the conventional hydrologic model to build a system that operates
at the catchment level displayed with hydrometric stations, that use the data from hydrometric
sensors networks (e.g., rainfall, river stage, river flow) captured, stored and administered
by an organization of interacting agents whose main aim is to perform flow forecasting and
awareness, and in so doing enhance the policy-making process at the watershed level.
Section one of this document surveys the status of the current research in hydrologic
modeling for the flood forecasting task. It is a journey through the background of related
concerns to the hydrological process, flood ontologies, management, and forecasting. The
section covers, to a certain extent, the techniques, methods, and theoretical aspects and
methods of hydrological modeling and their types, from the conventional models to the
present-day artificial intelligence prototypes, making special emphasis on the multi-agent
systems, as most recent modeling methodology in the hydrological sciences. However, it is
also underlined here that the section does not contribute to an all-inclusive revision, rather
its purpose is to serve as a framework for this sort of work and a path to underline the
significant aspects of the works.
In section two of the document, it is detailed the conceptual framework for the suggested
Multiagent system in support of flood forecasting. To accomplish this task, several works
need to be carried out such as the sketching and implementation of the systemâs framework
with the (Belief-Desire-Intention model) architecture for flood forecasting events within the
concept of the tropical river basin. Contributions of this proposed architecture are the
replacement of the conventional hydrologic modeling with the use of multi-agent systems,
which makes it quick for hydrometric time-series data administration and modeling of the
precipitation-runoff process which conveys to flood in a river course. Another advantage is
the user-friendly environment provided by the proposed multi-agent system platform graphical
interface, the real-time generation of graphs, charts, and monitors with the information
on the immediate event taking place in the catchment, which makes it easy for the viewer
with some or no background in data analysis and their interpretation to get a visual idea of
the information at hand regarding the flood awareness.
The required agents developed in this multi-agent system modeling framework for flood
forecasting have been trained, tested, and validated under a series of experimental tasks,
using the hydrometric series information of rainfall, river stage, and streamflow data collected
by the hydrometric sensor agents from the hydrometric sensors.Como se sabe, los problemas relacionados con la generaciĂłn de inundaciones, su control y
manejo, han sido tratados con herramientas tradicionales de modelado hidrolĂłgico enfocados
al estudio y anĂĄlisis de la relaciĂłn precipitaciĂłn-escorrentĂa, proceso fĂsico que es impulsado
por el ciclo hidrológico y el régimen climåtico y este esta directamente proporcional a la
generaciĂłn de crecidas. Dentro de la disciplina hidrolĂłgica, clasifican estas herramientas
de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos
empĂricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o
semi-distribuidos) dependiendo de la distribuciĂłn espacial y, por Ășltimo, los basados en la
fĂsica, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido,
clasifican las aplicaciones de predicciĂłn de caudal fluvial en la ingenierĂa de recursos hĂdricos
en dos tipos con respecto a los valores y parĂĄmetros que requieren en: modelos de procesos
basados en la fĂsica y la categorĂa de modelos impulsados por datos.
Los modelos basados en la fĂsica proporcionan una descripciĂłn detallada de la dinĂĄmica
relacionada con los aspectos fĂsicos que ocurren internamente entre los diferentes sistemas de
una cuenca hidrogrĂĄfica determinada. Sin embargo, aparte de ser complejos de implementar,
se basan completamente en algoritmos matemĂĄticos, y la comprensiĂłn de estas interacciones
requiere la abstracciĂłn de conceptos matemĂĄticos y la conceptualizaciĂłn de los procesos
fĂsicos que se entrelazan entre estos sistemas. AdemĂĄs, los modelos impulsados por datos no
requieren conocimiento de los procesos fĂsicos que gobiernan, sino que se basan Ășnicamente
en ecuaciones empĂricas que necesitan una gran cantidad de datos y requieren calibraciĂłn
de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos
de datos y de cĂłmo expresan los fenĂłmenos fĂsicos. La elaboraciĂłn de modelos hidrolĂłgicos
para el pronĂłstico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos
contratiempos importantes, dada la naturaleza estocĂĄstica de los fenĂłmenos hidrolĂłgicos, es
el desafĂo de implementar sistemas de pronĂłstico fĂĄciles de usar, reutilizables, robustos y
confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema
de la predicciĂłn de inundaciones. Sin embargo, en las Ășltimas dĂ©cadas, con el entorno
creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores
rara vez han intentado abordar la naturaleza estocĂĄstica de los eventos hidrolĂłgicos con la
aplicación de algunas de estas técnicas.
Dados los contratiempos en el pronĂłstico de inundaciones hidrolĂłgicas descritos anteriormente,
esta investigaciĂłn de tesis tiene como objetivo integrar los modelos hidrolĂłgicos,
basados en la fĂsica, hidrĂĄulicos e impulsados por datos bajo el paradigma de Sistemas de mĂșltiples agentes para el pronĂłstico de inundaciones por medio del bosquejo y desarrollo
del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicciĂłn de
inundaciones en el contexto de cuenca hidrogrĂĄfica tropical.
Con la apariciĂłn de las tecnologĂas de agentes, se han emprendido algunos enfoques
de simulaciĂłn recientes en la investigaciĂłn hidrolĂłgica con modelos basados en agentes y
sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificaciĂłn
de inundaciones, control y gestiĂłn de inundaciones y pronĂłstico de inundaciones, todos estos
enfocado a simulacros de evacuaciĂłn, y este Ășltimo no dirigido a la cuenca tropical, cuyo
rĂ©gimen hidrolĂłgico es extremadamente Ășnico.
En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas
multi-agente como un sustituto del modelado hidrolĂłgico convencional para construir un
sistema que opera a nivel de cuenca con estaciones hidrométricas desplegadas, que utilizan
los datos de redes de sensores hidromĂ©tricos (por ejemplo, lluvia , nivel del rĂo, caudal del
rĂo) capturado, almacenado y administrado por una organizaciĂłn de agentes interactuantes
cuyo objetivo principal es realizar pronĂłsticos de caudal y concientizaciĂłn para mejorar las
capacidades de soporte en la formulaciĂłn de polĂticas a nivel de cuenca hidrogrĂĄfica.
La primera secciĂłn de este documento analiza el estado del arte sobre la investigaciĂłn actual
en modelos hidrológicos para la tarea de pronóstico de inundaciones. Es un viaje a través
de los antecedentes preocupantes relacionadas con el proceso hidrolĂłgico, las ontologĂas de
inundaciones, la gestión y la predicción. El apartado abarca, en cierta medida, las técnicas,
mĂ©todos y aspectos teĂłricos y mĂ©todos del modelado hidrolĂłgico y sus tipologĂas, desde
los modelos convencionales hasta los prototipos de inteligencia artificial actuales, haciendo
hincapié en los sistemas multi-agente, como un enfoque de simulación reciente en la investigación
hidrolĂłgica. Sin embargo, se destaca que esta secciĂłn no contribuye a una revisiĂłn
integral, sino que su propĂłsito es servir de marco para este tipo de trabajos y una guĂa para
subrayar los aspectos significativos de los trabajos.
En la secciĂłn dos del documento, se detalla el marco de trabajo propuesto para el sistema
multi-agente para el pronĂłstico de inundaciones. Los trabajos realizados comprendieron el
diseño y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo
Creencia-Deseo-IntenciĂłn) para la predicciĂłn de eventos de crecidas dentro del concepto
de cuenca hidrogrĂĄfica tropical. Las contribuciones de esta arquitectura propuesta son el
reemplazo del modelado hidrolĂłgico convencional con el uso de sistemas multi-agente, lo
que agiliza la administración de las series de tiempo de datos hidrométricos y el modelado
del proceso de precipitaciĂłn-escorrentĂa que conduce a la inundaciĂłn en el curso de un rĂo.
Otra ventaja es el entorno amigable proporcionado por la interfaz grĂĄfica de la plataforma del
sistema multi-agente propuesto, la generaciĂłn en tiempo real de grĂĄficos, cuadros y monitores
con la informaciĂłn sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace
fĂĄcil para el espectador con algo o sin experiencia en anĂĄlisis de datos y su interpretaciĂłn
para tener una idea visual de la informaciĂłn disponible con respecto a la cogniciĂłn de las
inundaciones.
Los agentes necesarios desarrollados en este marco de modelado de sistemas multi-agente
para el pronóstico de inundaciones han sido entrenados, probados y validados en una serie de tareas experimentales, utilizando la información de la serie hidrométrica de datos de lluvia,
nivel del rĂo y flujo del curso de agua recolectados por los agentes sensores hidromĂ©tricos de
los sensores hidromĂ©tricos de campo.Programa de Doctorado en Ciencia y TecnologĂa InformĂĄtica por la Universidad Carlos III de MadridPresidente: MarĂa Araceli Sanchis de Miguel.- Secretario: Juan GĂłmez Romero.- Vocal: Juan Carlos Corrale
Data Mining in Smart Grids
Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin
Quantifying Forecast Uncertainty in the Energy Domain
This dissertation focuses on quantifying forecast uncertainties in the energy domain, especially for the electricity and natural gas industry. Accurate forecasts help the energy industry minimize their production costs. However, inaccurate weather forecasts, unusual human behavior, sudden changes in economic conditions, unpredictable availability of renewable sources (wind and solar), etc., represent uncertainties in the energy demand-supply chain. In the current smart grid era, total electricity demand from non-renewable sources influences by the uncertainty of the renewable sources. Thus, quantifying forecast uncertainty has become important to improve the quality of forecasts and decision making. In the natural gas industry, the task of the gas controllers is to guide the hourly natural gas flow in such a way that it remains within a certain daily maximum and minimum flow limits to avoid penalties. Due to inherent uncertainties in the natural gas forecasts, setting such maximum and minimum flow limits a day or more in advance is difficult. Probabilistic forecasts (cumulative distribution functions), which quantify forecast uncertainty, are a useful tool to guide gas controllers to make such tough decisions. Three methods (parametric, semi-parametric, and non-parametric) are presented in this dissertation to generate 168-hour horizon probabilistic forecasts for two real utilities (electricity and natural gas) in the US. Probabilistic forecasting is used as a tool to solve a real-life problem in the natural gas industry. A benchmark was created based on the existing solution, which assumes forecast error is normal. Two new probabilistic forecasting methods are implemented in this work without the normality assumption. There is no single popular evaluation technique available to assess probabilistic forecasts, which is one reason for peopleâs lack of interest in using probabilistic forecasts. Existing scoring rules are complicated, dataset dependent, and provide less emphasis on reliability (empirical distribution matches with observed distribution) than sharpness (the smallest distance between any two quantiles of a CDF). A graphical way to evaluate probabilistic forecasts along with two new scoring rules are offered in this work. The non-parametric and semi-parametric probabilistic forecasting methods outperformed the benchmark method during unusual days (difficult days to forecast) as well as on other days
Recommended from our members
Nature inspired computational intelligence for financial contagion modelling
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the âtransmissionâ of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Tradersâ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial marketâs parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market
Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments
In this doctoral dissertation characteristics of very large industrial real investments (VLIRI) are investigated and a special group of VLIRI is defined as giga-investments. The investment decision-making regarding to giga-investments is discussed from the points of view of discounted cash-flow based methods and real option valuation. Based on the bacground of establishing giga-investments, state-of-the-art in capital budgeting (including real options) and by applying fuzzy numbers a novel method for the evaluation and profitability analysis of giga-investments is presented. Application of the method is illustrated and issues regarding investment decision-making of large industrial real investments are discussed.Real Options; Fuzzy Real Option Valuation; Giga-Investments; Very Large Industrial Real Investments; Dissertation
Artificial neural networks for vibration based inverse parametric identifications: A review
Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNsâ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes
- âŠ