313 research outputs found
Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences
Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc
Artificial Intelligence and Cognitive Computing
Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that
Contextualized property market models vs. Generalized mass appraisals: An innovative approach
The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies
Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market
With the availability of high frequency data and new techniques for the management of noise in signals, we revisit the question, can we predict financial asset prices? The present work proposes an algorithm for next-step log-return prediction. Data in frequencies from 1 to 15 minutes, for 25 high capitalization assets in the Mexican market were used. The model applied consists on a wavelet followed by a Long Short-Term Memory neural network (LSTM). Application of either wavelets or neural networks in finance are common, the novelty comes from the application of the particular architecture proposed. The results show that, on average, the proposed LSTM neuro-wavelet model outperforms both an ARIMA model and a benchmark dense neural network model. We conclude that, although further research (in other stock markets, at higher frequencies, etc.) is in order, given the ever increasing technical capacity of market participants, the inclusion of the LSTM neuro-wavelet model is a valuable addition to the market participant toolkit, and might pose an advantage to traditional predictive tools.Modelo de neuro-onda para predicciĂłn de precios en datos de alta frecuencia en el Mercado Bursátil MexicanoCon la disponibilidad de datos de alta frecuencia y nuevas tĂ©cnicas para la filtraciĂłn de señales, es pertinente preguntarse una vez más Âżpodemos predecir los precios de los activos financieros? El presente trabajo propone un algoritmo para la predicciĂłn de retorno logarĂtmico del siguiente periodo. Se usan datos en frecuencias de 1 a 15 minutos, para 25 activos de alta capitalizaciĂłn en el mercado accionario mexicano. El modelo consiste en la aplicaciĂłn de una wavelet seguida de una red neuronal de tipo Long Short-Term Memory (LSTM). En la literatura comĂşnmente se encuentra el uso de wavelets o de redes neuronales en aplicaciones financieras, la novedad de nuestro trabajo radica en la arquitectura particular que proponemos. Los resultados muestran que, en promedio, el modelo de neuro-wavelet propuesto supera tanto a un modelo ARIMA como a un modelo de red neuronal densa de referencia. Podemos concluir que, aunque más investigaciĂłn es necesaria, dada la creciente capacidad tĂ©cnica actual de los participantes del mercado, la inclusiĂłn del modelo LSTM neuro – wavelet al abanico de herramientas disponibles es de mucho valor, pues podrĂa representar una ventaja sobre las herramientas predictivas tradicionales
Dynamic non-linear system modelling using wavelet-based soft computing techniques
The enormous number of complex systems results in the necessity of high-level and cost-efficient
modelling structures for the operators and system designers. Model-based approaches offer a very
challenging way to integrate a priori knowledge into the procedure. Soft computing based models
in particular, can successfully be applied in cases of highly nonlinear problems. A further reason
for dealing with so called soft computational model based techniques is that in real-world cases,
many times only partial, uncertain and/or inaccurate data is available.
Wavelet-Based soft computing techniques are considered, as one of the latest trends in system
identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based
approaches to model the non-linear dynamical systems in real world problems in conjunction with
possible twists and novelties aiming for more accurate and less complex modelling structure.
Initially, an on-line structure and parameter design has been considered in an adaptive Neuro-
Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy
rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus
(Monascus ruber van Tieghem) is examined against several other approaches for further
justification of the proposed methodology.
By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have
been introduced. Increasing the accuracy and decreasing the computational cost are both the
primary targets of proposed novelties. Modifying the synoptic weights by replacing them with
Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA)
comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for
the above challenges. These two models differ from the point of view of structure while they share
the same HLA scheme. The second approach contains an additional Multiplication layer, plus its
hidden layer contains several sub-WNNs for each input dimension. The practical superiority of
these extensions is demonstrated by simulation and experimental results on real non-linear
dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT)
whole milk, and consolidated with comprehensive comparison with other suggested schemes.
At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is
presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network
(FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a
modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from
the data by building accurate regression, but also for the identification of complex systems.
The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the
consequent parts of rules. In order to improve the function approximation accuracy and general
capability of the FWNN system, an efficient hybrid learning approach is used to adjust the
parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is
employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which
is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world
application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the
above technique
Dealing with Uncertainty: An Empirical Study on the Relevance of Renewable Energy Forecasting Methods
The increasing share of fluctuating renewable energy sources on the world-wide energy production leads to a rising public interest in dedicated forecasting methods. As different scientific communities are dedicated to that topic, many solutions are proposed but not all are suited for users from utility companies. We describe an empirical approach to analyze the scientific relevance of renewable energy forecasting methods in literature. Then, we conduct a survey amongst forecasting software providers and users from the energy domain and compare the outcomes of both studies
Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5. - 6. Dezember 2013
Dieser Tagungsband enthält die Beiträge des 23. Workshops Computational Intelligence des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA), der vom 5. - 6. Dezember 2013 in Dortmund stattgefunden hat. Im Fokus stehen Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren
Fuzzy-wavelet method for time series analysis
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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