Showing papers on "Neuro-fuzzy published in 2006"

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Abstract: Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification

Abstract: A new dynamic time-delay fuzzy wavelet neural network model is presented for nonparametric identification of structures using the nonlinear autoregressive moving average with exogenous inputs approach. The model is based on the integration of four different computing concepts: dynamic time delay neural network, wavelet, fuzzy logic, and the reconstructed state space concept from the chaos theory. Noise in the signals is removed using the discrete wavelet packet transform method. In order to preserve the dynamics of time series, the reconstructed state space concept from the chaos theory is employed to construct the input vector. In addition to denoising, wavelets are employed in combination with two soft computing techniques, neural networks and fuzzy logic, to create a new pattern recognition model to capture the characteristics of the time series sensor data accurately and efficiently. The model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively. Experimental results on a five-story steel frame are employed to validate the computational model and demonstrate its accuracy and efficiency.

Fuzzy Databases: Modeling, Design, and Implementation

Abstract: Sample of Contents: State of the Art in Fuzzy Database Modeling FuzzyEER Mapping FuzzyEER Model Concepts to Relations FSQL: A Fuzzy SQL for Fuzzy Databases.

The application of neural networks to forecast fuzzy time series

Abstract: Fuzzy time series models have been applied to handle nonlinear problems. To forecast fuzzy time series, this study applies a backpropagation neural network because of its nonlinear structures. We propose two models: a basic model using a neural network approach to forecast all of the observations, and a hybrid model consisting of a neural network approach to forecast the known patterns as well as a simple method to forecast the unknown patterns. The stock index in Taiwan for the years 1991–2003 is chosen as the forecasting target. The empirical results show that the hybrid model outperforms both the basic and a conventional fuzzy time series models.

POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction

Abstract: Although much research has been done over the decades on the formulation of statistical regression models for road traffic relationships, they have been largely unsuitable due to the complexity of traffic characteristics. Traffic engineers have resorted to alternative methods such as neural networks, but despite some promising results, the difficulties in their design and implementation remain unresolved. In addition, the opaqueness of trained networks prevents understanding the underlying models. Fuzzy neural networks, which combine the complementary capabilities of both neural networks and fuzzy logic, thus constitute a more promising technique for modeling traffic flow. This paper describes the application of a specific class of fuzzy neural network known as the pseudo outer-product fuzzy neural network using the truth-value-restriction method (POPFNN-TVR) for short-term traffic flow prediction. The obtained results highlight the capability of POPFNN-TVR in fuzzy knowledge extraction and generalization from input data as well its high degree of prediction capability as compared to traditional feedforward neural networks using backpropagation learning.

Support-vector-based fuzzy neural network for pattern classification

Abstract: Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network (SVFNN) is proposed for pattern classification in this paper. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. A learning algorithm consisting of three learning phases is developed to construct the SVFNN and train its parameters. In the first phase, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the second phase, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. In the third phase, the relevant fuzzy rules are selected by the proposed reducing fuzzy rule method. To investigate the effectiveness of the proposed SVFNN classification, it is applied to the Iris, Vehicle, Dna, Satimage, Ijcnn1 datasets from the UCI Repository, Statlog collection and IJCNN challenge 2001, respectively. Experimental results show that the proposed SVFNN for pattern classification can achieve good classification performance with drastically reduced number of fuzzy kernel functions.

Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry

Abstract: Reliable prediction of sales can improve the quality of business strategy. In this research, fuzzy logic and artificial neural network are integrated into the fuzzy back-propagation network (FBPN) for sales forecasting in Printed Circuit Board (PCB) industry. The fuzzy back propagation network is constructed to incorporate production-control expert judgments in enhancing the model's performance. Parameters chosen as inputs to the FBPN are no longer considered as of equal importance, but some sales managers and production control experts are requested to express their opinions about the importance of each input parameter in predicting the sales with linguistic terms, which can be converted into pre-specified fuzzy numbers. The proposed system is evaluated through the real world data provided by a printed circuit board company and experimental results indicate that the Fuzzy back-propagation approach outperforms other three different forecasting models in MAPE measures.

Convergent results about the use of fuzzy simulation in fuzzy optimization problems

Abstract: We discuss the convergence of fuzzy simulation as it is employed in fuzzy optimization problems. Several convergence concepts for sequences of fuzzy variables are defined such as convergence in optimistic value. A new approach to approximating essentially bounded fuzzy variables with continuous possibility distributions is introduced. Applying the proposed approximation method to our previous work, we prove three convergence theorems about the use of fuzzy simulation in computing the credibility of a fuzzy event, finding the optimistic value of a return function, and calculating the expected value of a fuzzy variable

Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

Abstract: A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm

Uncertainty and the Semantic Web

Abstract: The semantic Web must handle information from applications that have special knowledge representation needs and that face uncertain, imprecise knowledge. More precisely, some applications deal with random information and events, others deal with imprecise and fuzzy knowledge, and still others deal with missing or distorted information - resulting in uncertainty. To deal with uncertainty in the semantic Web and its applications, many researchers have proposed extending OWL and the description logic (DL) formalisms with special mathematical frameworks. Researchers have proposed probabilistic, possibilistic, and fuzzy extensions, among others. Researchers have studied fuzzy extensions most extensively, providing impressive results on semantics, reasoning algorithms, and implementations. Building on these results, we've created a fuzzy extension to OWL called Fuzzy OWL. Fuzzy OWL can capture imprecise and vague knowledge. Moreover, our reasoning platform, fuzzy reasoning engine (FiRE), lets Fuzzy OWL capture and reason about such knowledge

The strategy of building a flood forecast model by neuro‐fuzzy network

Abstract: A methodology is proposed for constructing a flood forecast model using the adaptive neuro-fuzzy inference system (ANFIS). This is based on a self-organizing rule-base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall-runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self-constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back-propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model. Copyright © 2005 John Wiley & Sons, Ltd.

Fundamentals of Statistics with Fuzzy Data

Abstract: Introduction.- Set-valued Data.- Modeling of fuzzy data.- Random fuzzy sets.- Aspect of statistical Inference.- Convergence of random fuzzy sets.- Fuzzy Statistical Analysis and Estimation.- Testing Hypothesis with Fuzzy Data.- Fuzzy Time Series Analysis and Forecasting.

Fuzzy Rough Sets and Multiple-Premise Gradual Decision Rules

Abstract: We propose a new fuzzy rough set approach which, differently from most known fuzzy set extensions of rough set theory, does not use any fuzzy logical connectives (t-norm, t-conorm, fuzzy implication). As there is no rationale for a particular choice of these connectives, avoiding this choice permits to reduce the part of arbitrary in the fuzzy rough approximation. Another advantage of the new approach is that it is based on the ordinal properties of fuzzy membership degrees only. The concepts of fuzzy lower and upper approximations are thus proposed, creating a base for induction of fuzzy decision rules having syntax and semantics of gradual rules. The proposed approach to rule induction is also interesting from the viewpoint of philosophy supporting data mining and knowledge discovery, because it is concordant with the method of concomitant variations by John Stuart Mill. The decision rules are induced from lower and upper approximations defined for positive and negative relationships between credibility degrees of multiple premises, on one hand, and conclusion, on the other hand.

Using Uncertainty Bounds in the Design of an Embedded Real-Time Type-2 Neuro-Fuzzy Speed Controller for Marine Diesel Engines

Abstract: Marine diesel engines operate in highly dynamic and uncertain environments, hence they require robust and accurate speed controllers that can handle the encountered uncertainties. Type-2 Fuzzy Logic Controllers (FLCs) can handle such uncertainties; however they have a computational overhead associated with the iterative type-reduction process which can diminish the FLC real-time performance. Furthermore, manually designing a type-2 FLC is a difficult task particularly as the number of membership function parameters and rules increase. In this paper, we will introduce an embedded Real-Time Type-2 Neuro-Fuzzy Controller (RT2NFC) which overcomes the iterative type-reduction overhead and learns the parameters of interval type-2 FLC for marine engines. We have performed numerous experiments on a real diesel engine testing platform in which we compared our RT2NFC to a T2NFC based on the iterative type reduction procedure. Both T2NFCs were embedded on an industrial microcontroller platform where they handled the uncertainties to produce accurate and robust speed controllers that outperformed the currently used commercial engine controller. The RT2NFC gave approximately the same control response as the T2NFC, whilst the RT2NFC avoided the type-reduction overhead thus giving a faster real-time response.

Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases

Abstract: This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB/sup -1/ allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB/sup -1/ model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB/sup -1/ has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB/sup -1/ model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB/sup -1/ converged in less than one minute for all the databases described in the case study.

Fuzzy multi-attribute decision-making method based on eigenvector of fuzzy attribute evaluation space

Abstract: For fuzzy multi-attribute decision-making, a fuzzy symmetry matrix, by referring to covariance definition of random variables, is constructed as attribute evaluation space based on fuzzy decision-making matrix. Improving the fuzzy Analytic Hierarchy Process (AHP) method is proposed by using the approximate fuzzy eigenvector of such fuzzy symmetry matrix. This algorithm reflects the dispersed projection of decision information in general. It has better objectivity and resolving power for the decision-making. This algorithm is used for illustration and comparison with other methods. The results are applied in an example to illustrate that this algorithm is more efficient and objective for multi-attribute decision-making application.

Observability and decentralized control of fuzzy discrete-event systems

Abstract: Fuzzy discrete-event systems as a generalization of (crisp) discrete-event systems have been introduced in order that it is possible to effectively represent uncertainty, imprecision, and vagueness arising from the dynamic of systems. A fuzzy discrete-event system has been modeled by a fuzzy automaton; its behavior is described in terms of the fuzzy language generated by the automaton. In this paper, we are concerned with the supervisory control problem for fuzzy discrete-event systems with partial observation. Observability, normality, and co-observability of crisp languages are extended to fuzzy languages. It is shown that the observability, together with controllability, of the desired fuzzy language is a necessary and sufficient condition for the existence of a partially observable fuzzy supervisor. When a decentralized solution is desired, it is proved that there exist local fuzzy supervisors if and only if the fuzzy language to be synthesized is controllable and co-observable. Moreover, the infimal controllable and observable fuzzy superlanguage, and the supremal controllable and normal fuzzy sublanguage are also discussed. Simple examples are provided to illustrate the theoretical development

Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox

Abstract: In most fuzzy systems, the completeness of the fuzzy rule base is required to generate meaningful output when classical fuzzy reasoning methods are applied. This means, in other words, that the fuzzy rule base has to cover all possible inputs. Regardless of the way of rule base construction, be it created by human experts or by an automated manner, often incomplete rule bases are generated. One simple solution to handle sparse fuzzy rule bases and to make infer reasonable output is the application of fuzzy rule interpolation (FRI) methods. In this paper, we present a Fuzzy Rule Interpolation Matlab Toolbox, which is freely available. With the introduction of this Matlab Toolbox, different FRI methods can be used for different real time applications, which have sparse or incomplete fuzzy rule base.

Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling

Abstract: One of the problems associated to linguistic fuzzy modeling is its lack of accuracy when modeling some complex systems. To overcome this problem, many different possibilities of improving the accuracy of linguistic fuzzy modeling have been considered in the specialized literature. We will call these approaches as basic refinement approaches. In this work, we present a short study of how these basic approaches can be combined to obtain new hybrid approaches presenting a better trade-off between interpretability and accuracy. As an example of application of these kinds of systems, we analyze seven hybrid approaches to develop accurate and still interpretable fuzzy rule-based systems, which will be tested considering two real-world problems.

Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge

Abstract: This study proposes a knowledge discovery model that integrates the modification of the fuzzy transaction data-mining algorithm (MFTDA) and the Adaptive-Network-Based Fuzzy Inference Systems (ANFIS) for discovering implicit knowledge in the fuzzy database more efficiently and presenting it more concisely. A prototype was built for testing the feasibility of the model. The testing data are from a company's human resource management department. The results indicated that the generated rules (knowledge) are useful in supporting the company to predict its employees' future performance and then assign proper persons for appropriate positions and projects. Furthermore, the convergence of ANFIS in the model was proven to be more efficient than a generic fuzzy artificial neural network.