Showing papers on "Neuro-fuzzy published in 2002"

Expected value of fuzzy variable and fuzzy expected value models

Abstract: This paper will present a novel concept of expected values of fuzzy variables, which is essentially a type of Choquet integral and coincides with that of random variables. In order to calculate the expected value of general fuzzy variable, a fuzzy simulation technique is also designed. Finally, we construct a spectrum of fuzzy expected value models, and integrate fuzzy simulation, neural network, and genetic algorithms to produce a hybrid intelligent algorithm for solving general fuzzy expected value models.

Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases

Abstract: Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach Genetic Fuzzy Rule-Based Systems Based on the lterative Rule Learning Approach Other Genetic Fuzzy Rule-Based System Other Kinds of Evolutionary Fuzzy Systems Applications.

Fuzzy Relational Systems: Foundations and Principles

Abstract: From the Publisher: Fuzzy Relational Systems: Foundations and Principles presents a general theory of fuzzy relational systems and concentrates on selected general issues of fuzzy relational modeling in the framework of the developed theory. The text discusses phenomena hidden in the ordinary bivalent case, as well as new topics in fuzzy relational systems, such as object-attribute fuzzy relations and fuzzy concept latices, similarity, and fuzzy closure operators. Both mathematicians and engineers will find Fuzzy Relational Systems to be an invaluable teaching and reference resource in modeling and fuzzy logic.

A recurrent fuzzy-neural model for dynamic system identification

Abstract: This paper presents a fuzzy modeling approach for identification of dynamic systems. In particular, a new fuzzy model, the Dynamic Fuzzy Neural Network (DFNN), consisting of recurrent TSK rules, is developed. The premise and defuzzification parts are static while the consequent parts of the fuzzy rules are recurrent neural networks with internal feedback and time delay synapses. The network is trained by means of a novel learning algorithm, named Dynamic-Fuzzy Neural Constrained Optimization Method (D-FUNCOM), based on the concept of constrained optimization. The proposed algorithm is general since it can be applied to locally as well as fully recurrent networks, regardless of their structures. An adaptation mechanism of the maximum parameter change is presented as well. The proposed dynamic model, equipped with the learning algorithm, is applied to several temporal problems, including modeling of a NARMA process and the noise cancellation problem. Performance comparisons are conducted with a series of static and dynamic systems and some existing recurrent fuzzy models. Simulation results show that DFNN compares favorably with its competing rivals and thus it can be considered for efficient system identification.

An Introduction to Fuzzy Logic and Fuzzy Sets

Abstract: 1 Introduction.- 2 Logic.- 3 Fuzzy Sets.- 4 Fuzzy Numbers.- 5 Fuzzy Equations.- 6 Fuzzy Inequalities.- 7 Fuzzy Relations.- 8 Fuzzy Functions.- 9 Fuzzy Plane Geometry.- 10 Fuzzy Trigonometry.- 11 Systems of Fuzzy Linear Equations.- 12 Possibility Theory.- 13 Neural Nets.- 14 Approximate Reasoning.- 15 Genetic Algorithms.- 16 Fuzzy Optimization.- List of Figures.- List of Tables.

Self-adaptive neuro-fuzzy inference systems for classification applications

Abstract: This paper presents a self-adaptive neuro-fuzzy inference system (SANFIS) that is capable of self-adapting and self-organizing its internal structure to acquire a parsimonious rule-base for interpreting the embedded knowledge of a system from the given training data set. A connectionist topology of fuzzy basis functions with their universal approximation capability is served as a fundamental SANFIS architecture that provides an elasticity to be extended to all existing fuzzy models whose consequent could be fuzzy term sets, fuzzy singletons, or functions of linear combination of input variables. Without a priori knowledge of the distribution of the training data set, a novel mapping-constrained agglomerative clustering algorithm is devised to reveal the true cluster configuration in a single pass for an initial SANFIS construction, estimating the location and variance of each cluster. Subsequently, a fast recursive linear/nonlinear least-squares algorithm is performed to further accelerate the learning convergence and improve the system performance. Good generalization capability, fast learning convergence and compact comprehensible knowledge representation summarize the strength of SANFIS. Computer simulations for the Iris, Wisconsin breast cancer, and wine classifications show that SANFIS achieves significant improvements in terms of learning convergence, higher accuracy in recognition, and a parsimonious architecture.

Fuzzy data mining and genetic algorithms applied to intrusion detection

Abstract: We are developing a prototype intelligent intrusion detection system (IIDS) to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic and genetic algorithms. This system combines both anomaly based intrusion detection using fuzzy data mining techniques and misuse detection using traditional rule-based expert system techniques. The anomaly-based components are developed using fuzzy data mining techniques. They look for deviations from stored patterns of normal behavior. Genetic algorithms are used to tune the fuzzy membership functions and to select an appropriate set of features. The misuse detection components look for previously described patterns of behavior that are likely to indicate an intrusion. Both network traffic and system audit data are used as inputs for both components.

Evolving Fuzzy Classifiers for Intrusion Detection

Abstract: The normal and the abnormal behaviors in networked computers are hard to predict as the boundaries cannot be well defined This prediction process may generate false alarms in many anomaly based intrusion detection systems However, with fuzzy logic, the false alarm rate in determining intrusive activities can be reduced; a set of fuzzy rules (non-crisp fuzzy classifiers) can be used to define the normal and abnormal behavior in a computer network, and a fuzzy inference algorithm can be applied over such rules to determine when an intrusion is in progress The main problem with this approach is to generate good fuzzy classifiers to detect intrusions This paper proposes a technique to generate fuzzy classifiers using genetic algorithms that can detect anomalies and some specific intrusions The main idea is to evolve two rules, one for the normal class and other for the abnormal class using a profile data set (a preprocessed DARPA data set is used (1)) with information related to the computer network during the normal behavior and during intrusive (abnormal) behavior This paper exhibits some results and reports the performance of evolved fuzzy classifiers in intrusion detection

A First Course in Fuzzy and Neural Control

Abstract: A PRELUDE TO CONTROL THEORY An Ancient Control System Examples of Control Problems Open-Loop Control Systems Closed-Loop Control Systems Stable and Unstable Systems A Look at Controller Design Exercises and Projects MATHEMATICAL MODELS IN CONTROL Introductory Examples: Pendulum Problems State Variables and Linear Systems Controllability and Observability Stability Controller Design State Variable Feedback Control Second-Order Systems Higher-Order Systems Proportional-Integral-Derivative Control Nonlinear Control Systems Linearization Exercises and Projects FUZZY LOGIC FOR CONTROL Fuzziness and Linguistic Rules Fuzzy Sets in Control Combining Fuzzy Sets Sensitivity of Functions Combining Fuzzy Rules Truth Tables for Fuzzy Logic Fuzzy Partitions Fuzzy Relations Defuzzification Level Curves and Alpha-Cuts Universal Approximation Exercises and Projects FUZZY CONTROL A Fuzzy Controller for an Inverted Pendulum Main Approaches to Fuzzy Control Stability of Fuzzy Control Systems Fuzzy Controller Design Exercises and Projects NEURAL NETWORKS FOR CONTROL What is a Neural Network? . Implementing Neural Networks Learning Capability The Delta Rule The Back Propagation Algorithm Example: Training a Neural Network Practical Issues in Training Exercises and Projects NEURAL CONTROL Why Neural Networks in Control Inverse Dynamics Neural Networks in Direct Neural Control Example: Temperature Control Neural Networks in Indirect Neural Control Exercises and Projects FUZZY-NEURAL AND NEURAL-FUZZY CONTROL Fuzzy Concepts in Neural Networks Basic Principles of Fuzzy-Neural Systems Basic Principles of Neural-Fuzzy Systems Generating Fuzzy Rules and Membership Functions Exercises and Projects APPLICATIONS A Survey of Industrial Applications Cooling Scheme for Laser Materials Color Quality Processing Identification of Trash in Cotton Integrated Pest Management Systems Comments Bibliography Index

Fuzzy decision making in modeling and control

Abstract: Fuzzy Decision Making Fuzzy Decision Functions Fuzzy Aggregated Membership Control Modeling and Identification Fuzzy Decision Making for Modeling Fuzzy Model-Based Control Performance Criteria Model-Based Control with Fuzzy Decision Functions Derivative-Free Optimization Advanced Optimization Issues Application Example Future Developments Appendices: Model-Based Predictive Control Nonlinear Internal Model Control.

GenSoFNN: a generic self-organizing fuzzy neural network

Abstract: Existing neural fuzzy (neuro-fuzzy) networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data. No initial rule base needs to be specified prior to training. A cluster analysis is first performed on the training data and the fuzzy rules are subsequently derived through the proper connections of these computed clusters. However, most existing neural fuzzy systems (whether they belong to the first or second group) encountered one or more of the following major problems. They are (1) inconsistent rule-base; (2) heuristically defined node operations; (3) susceptibility to noisy training data and the stability-plasticity dilemma; and (4) needs for prior knowledge such as the number of clusters to be computed. Hence, a novel neural fuzzy system that is immune to the above-mentioned deficiencies is proposed in this paper. This new neural fuzzy system is named the generic self-organizing fuzzy neural network (GenSoFNN). The GenSoFNN network has strong noise tolerance capability by employing a new clustering technique known as discrete incremental clustering (DIC). The fuzzy rule base of the GenSoFNN network is consistent and compact as GenSoFNN has built-in mechanisms to identify and prune redundant and/or obsolete rules. Extensive simulations were conducted using the proposed GenSoFNN network and its performance is encouraging when benchmarked against other neural and neural fuzzy systems.

Can neural networks be easily interpreted in software cost estimation

Abstract: Software development effort estimation with the aid of neural networks has generally been viewed with skepticism by a majority of the software cost estimation community. Although, neural networks have shown their strengths in solving complex problems, their shortcoming of being 'black boxes' models has prevented them from being accepted as a common practice for cost estimation. In this paper, we study the interpretation of cost estimation models based on a backpropagation three layer perceptron network. Our proposed idea comprises mainly of the use of a method that maps this neural network to a fuzzy rule based system. Consequently, if the obtained fuzzy rules are easily interpreted, the neural network will also be easy to interpret. Our case study is based on the COCOMO'81 dataset.

Toward Fuzzy Optimization without Mathematical Ambiguity

Abstract: Fuzzy programming has been discussed widely in literature and applied in such various disciplines as operations research, economic management, business administration, and engineering. The main purpose of this paper is to present a brief review on fuzzy programming models, and classify them into three broad classes: expected value model, chance-constrained programming and dependent-chance programming. In order to solve general fuzzy programming models, a hybrid intelligent algorithm is also documented. Finally, some related topics are discussed.

Enhanced genetic algorithm-based fuzzy multi-objective approach to distribution network reconfiguration

Abstract: An enhanced genetic algorithm (EGA)-based fuzzy multi-objective approach to solve a network reconfiguration problem in a radial distribution system is presented. Maximising the fuzzy satisfaction allows the operator to simultaneously consider the multiple objectives of the network reconfiguration to minimise power loss, violation of voltage and current constraints, as well as switching number, while subject to a radial network structure in which all loads must be energised. The optimisation technique of the EGA is then adopted to solve the fuzzy multi-objective problem efficiently. Test results verify the feasibility of applying the proposed method to manipulate the combinatorial optimisation network reconfiguration in distribution systems.

Advanced Fuzzy Systems Design and Applications

Abstract: This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms.

Fuzzy Mathematics in Economics and Engineering

Abstract: Fuzzy Sets.- Solving Fuzzy Equations.- Fuzzy Mathematics in Finance.- Fuzzy Non-Linear Regression.- Operations Research.- Fuzzy Differential Equations.- Fuzzy Difference Equations.- Fuzzy Partial Differential Equations.- Fuzzy Eigenvalues.- Fuzzy Integral Equations.- Summary and Conclusions.- Evolutionary Algorithms.

Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation

Abstract: A fuzzy knowledge-based network is developed based on the linguistic rules extracted from a fuzzy decision tree. A scheme for automatic linguistic discretization of continuous attributes, based on quantiles, is formulated. A novel concept for measuring the goodness of a decision tree, in terms of its compactness (size) and efficient performance, is introduced. Linguistic rules are quantitatively evaluated using new indices. The rules are mapped to a fuzzy knowledge-based network, incorporating the frequency of samples and depth of the attributes in the decision tree. New fuzziness measures, in terms of class memberships, are used at the node level of the tree to take care of overlapping classes. The effectiveness of the system, in terms of recognition scores, structure of decision tree, performance of rules, and network size, is extensively demonstrated on three sets of real-life data.

A neuro-fuzzy approach to short-term load forecasting in a price-sensitive environment

Abstract: This paper presents a new approach to short-term load forecasting in a deregulated and price-sensitive environment. A real-time pricing type scenario is envisioned where energy prices could change on an hourly basis with the consumer having the ability to react to the price signal through shifting his electricity usage from expensive hours to other times when possible. The load profile under this scenario would have different characteristics compared to that of the regulated, fixed-price era. Consequently, short-term load forecasting models customized on price-insensitive (PIS) historical data of regulated era would no longer be able to perform well. In this work, a price-sensitive (PS) load forecaster is developed. This forecaster consists of two stages, an artificial neural network based PIS load forecaster followed by a fuzzy logic (FL) system that transforms the PIS load forecasts of the first stage into PS forecasts. The first stage forecaster is a widely used forecaster in industry known as ANNSTLF. For the FL system of the second stage, a genetic algorithm based approach is developed to automatically optimize the number of rules and the number and parameters of the fuzzy membership functions. Another FL system is developed to simulate PS load data from the PIS historical data of a utility. This new forecaster termed NFSTLF is tested on three PS database and it is shown that it produces superior results to the PIS ANNSTLF.

Interpretation of artificial neural networks by means of fuzzy rules

Abstract: This paper presents an extension of the method presented by Benitez et al (1997) for extracting fuzzy rules from an artificial neural network (ANN) that express exactly its behavior. The extraction process provides an interpretation of the ANN in terms of fuzzy rules. The fuzzy rules presented are in accordance with the domain of the input variables. These rules use a new operator in the antecedent. The properties and intuitive meaning of this operator are studied. Next, the role of the biases in the fuzzy rule-based systems is analyzed. Several examples are presented to comment on the obtained fuzzy rule-based systems. Finally, the interpretation of ANNs with two or more hidden layers is also studied.

A Fast and Efficient Algorithm for Training Radial Basis Function Neural Networks Based on a Fuzzy Partition of the Input Space

Abstract: The popular radial basis function (RBF) neural network architecture and a new fast and efficient method for training such a network are used to model nonlinear dynamical multi-input multi-output (M...

Weighted fuzzy reasoning using weighted fuzzy Petri nets

Abstract: This paper presents a Weighted Fuzzy Petri Net model (WFPN) and proposes a weighted fuzzy reasoning algorithm for rule-based systems based on Weighted Fuzzy Petri Nets. The fuzzy production rules in the knowledge base of a rule-based system are modeled by Weighted Fuzzy Petri Nets, where the truth values of the propositions appearing in the fuzzy production rules and the certainty factors of the rules are represented by fuzzy numbers. Furthermore, the weights of the propositions appearing in the rules are also represented by fuzzy numbers. The proposed weighted fuzzy reasoning algorithm can allow the rule-based systems to perform fuzzy reasoning in a more flexible and more intelligent manner.

COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules

Abstract: This paper introduces a new learning methodology to quickly generate accurate and simple linguistic fuzzy models: the cooperative rules (COR) methodology. It acts on the consequents of the fuzzy rules to find those that are best cooperating. Instead of selecting the consequent with the highest performance in each fuzzy input subspace, as ad-hoc data-driven methods usually do, the COR methodology considers the possibility of using another consequent, different from the best one, when it allows the fuzzy model to be more accurate thanks to having a rule set with the best cooperation. Our proposal has shown good results in solving three different applications when compared to other methods.

An improved algorithm for fuzzy data mining for intrusion detection

Abstract: We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection. We describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency. We use sets of fuzzy association rules that are mined from network audit data as models of "normal behavior." To detect anomalous behavior, we generate fuzzy association rules from new audit data and compute the similarity with sets mined from "normal" data. If the similarity values are below a threshold value, an alarm is issued. We describe an algorithm for computing fuzzy association rules based on Borgelt's (2001) prefix trees, modifications to the computation of support and confidence of fuzzy rules, a new method for computing the similarity of two fuzzy rule sets, and feature selection and optimization with genetic algorithms. Experimental results demonstrate that we can achieve better running time and accuracy with these modifications.