250 research outputs found
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionāmaking process as well as strategic planning. In this study, a prototype asymmetricābased neuroāfuzzy network (AGFINN) architecture has been implemented for shortāterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellāestablished learningābased models
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 TakagiāSugenoāKang (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 ācurse of dimensionalityā 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 beefās 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 functionās 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. AGFINNās MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems
Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose
Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20Ā°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilag
Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems
Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic
fuzzy variables as antecedents and consequent to represent human understandable
knowledge. They have been applied to various applications and areas throughout
the soft computing literature. However, FRBSs suffers from many drawbacks such
as uncertainty representation, high number of rules, interpretability loss,
high computational time for learning etc. To overcome these issues with FRBSs,
there exists many extensions of FRBSs. This paper presents an overview and
literature review of recent trends on various types and prominent areas of
fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy
system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for
big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which
use cluster centroids as fuzzy rules. The review is for years 2010-2021. This
paper also highlights important contributions, publication statistics and
current trends in the field. The paper also addresses several open research
areas which need further attention from the FRBSs research community.Comment: 49 pages, Accepted for publication in ijf
A Novel Robust Algorithm for Information Security Risk Evaluation
Abstract As computer becomes popular and internet advances rapidly, informatio
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
Variational inference for robust sequential learning of multilayered perceptron neural network
U radu je prikazan i izveden novi sekvencijalni algoritam za obuÄavanje viÅ”eslojnog perceptrona u prisustvu autlajera. Autlajeri predstavljaju znaÄajan problem, posebno ukoliko sprovodimo sekvencijalno obuÄavanje ili obuÄavanje u realnom vremenu. Linearizovani Kalmanov filtar robustan na autlajere (LKF-RA), je statistiÄki generativni model u kome je matrica kovarijansi Å”uma merenja modelovana kao stohastiÄki proces, a apriorna informacija usvojena kao inverzna ViÅ”artova raspodela. IzvoÄenje svih jednakosti je bazirano na prvim principima Bajesovske metodologije. Da bi se reÅ”io korak modifikacije primenjen je varijacioni metod, u kome reÅ”enje problema tražimo u familiji raspodela odgovarajuÄe funkcionalne forme. Eksperimentalni rezultati primene LKF-RA, dobijeni koriÅ”Äenjem stvarnih vremenskih serija, pokazuju da je LKF-RA bolji od konvencionalnog linearizovanog Kalmanovog filtra u smislu generisanja niže greÅ”ke na test skupu podataka. ProseÄna vrednost poboljÅ”anja odreÄena u eksperimentalnom procesu je 7%.We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time. Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is given as inverse Wishart distribution. Derivation of expressions comes straight form first principles, within Bayesian framework. Analytical intractability of Bayes' update step is solved using Variational Inference (VI). Experimental results obtained using real world stochastic data show that MLP network trained with proposed algorithm achieves low error and average improvement rate of 7% when compared directly to conventional EKF learning algorithm
Variational inference for robust sequential learning of multilayered perceptron neural network
U radu je prikazan i izveden novi sekvencijalni algoritam za obuÄavanje viÅ”eslojnog perceptrona u prisustvu autlajera. Autlajeri predstavljaju znaÄajan problem, posebno ukoliko sprovodimo sekvencijalno obuÄavanje ili obuÄavanje u realnom vremenu. Linearizovani Kalmanov filtar robustan na autlajere (LKF-RA), je statistiÄki generativni model u kome je matrica kovarijansi Å”uma merenja modelovana kao stohastiÄki proces, a apriorna informacija usvojena kao inverzna ViÅ”artova raspodela. IzvoÄenje svih jednakosti je bazirano na prvim principima Bajesovske metodologije. Da bi se reÅ”io korak modifikacije primenjen je varijacioni metod, u kome reÅ”enje problema tražimo u familiji raspodela odgovarajuÄe funkcionalne forme. Eksperimentalni rezultati primene LKF-RA, dobijeni koriÅ”Äenjem stvarnih vremenskih serija, pokazuju da je LKF-RA bolji od konvencionalnog linearizovanog Kalmanovog filtra u smislu generisanja niže greÅ”ke na test skupu podataka. ProseÄna vrednost poboljÅ”anja odreÄena u eksperimentalnom procesu je 7%.We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time. Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is given as inverse Wishart distribution. Derivation of expressions comes straight form first principles, within Bayesian framework. Analytical intractability of Bayes' update step is solved using Variational Inference (VI). Experimental results obtained using real world stochastic data show that MLP network trained with proposed algorithm achieves low error and average improvement rate of 7% when compared directly to conventional EKF learning algorithm
A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose
Food product safety is one of the most promising
areas for the application of electronic noses. The performance
of a portable electronic nose has been evaluated in monitoring
the spoilage of beef fillet stored aerobically at different storage
temperatures (0, 4, 8, 12, 16 and 20Ā°C). This paper proposes a
fuzzy-wavelet neural network model which incorporates a
clustering pre-processing stage for the definition of fuzzy rules.
The dual purpose of the proposed modeling approach is not
only to classify beef samples in the respective quality class (i.e.
fresh, semi-fresh and spoiled), but also to predict their
associated microbiological population directly from volatile
compounds fingerprints. Comparison results indicated that the
proposed modeling scheme could be considered as a valuable
detection methodology in food microbiolog
- ā¦