Ho-Sung Park

Hybrid identification in fuzzy-neural networks

This paper introduces an identification method for nonlinear models in the form of Fuzzy-Neural Networks (FNN). In this model, we use two forms of the fuzzy inference methods--a simplified and linear fuzzy inference, and exploit a standard Error Back Propagation learning algorithm. The FNN modeling and identification environment realizes parameter identification through a synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a Hard C-Means (HCM) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out by combining both genetic optimization (genetic algorithm, GA) and the improved complex method. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model with sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process and NOx emission process data of gas turbine power plant).

Multi-layer hybrid fuzzy polynomial neural networks: a design in the framework of computational intelligence

We introduce a new architecture of hybrid fuzzy polynomial neural networks (HFPNN) that is based on a genetically optimized multi-layer perceptron and develop their comprehensive design methodology involving mechanisms of genetic optimization. The construction of HFPNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the resulting genetically optimized HFPNN (namely gHFPNN) results from a synergistic usage of the hybrid system generated by combining fuzzy polynomial neurons (FPNs)-based fuzzy neural networks (FNN) with polynomial neurons (PNs)-based polynomial neural networks (PNN). The design of the conventional HFPNN exploits the extended group method of data handling (GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The augmented gHFPNN results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of HFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFPNN is quantified through experimentation where we exploit data coming from processes of pH neutralization and NOx emission. These datasets have already been used quite intensively in fuzzy and neurofuzzy modeling. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models.

Rule-based multi-FNN identification with the aid of evolutionary fuzzy granulation

Abstract In this paper, we introduce a category of Multi-Fuzzy-Neural Networks (Multi-FNNs) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs are based on a concept of fuzzy rule-based FNNs that use H ard C - M eans (HCM) clustering and evolutionary fuzzy granulation and exploit linear inference being treated as a generic inference mechanism of approximate reasoning. By this nature, this FNN model is geared toward capturing relationships between information granules–fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership functions) becomes an important design feature of the FNN model that contributes to its structural and parametric optimization. The genetically guided global optimization is then augmented by more refined gradient-based learning mechanisms such as a standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the experimental data, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates, and momentum coefficients) are adjusted using genetic algorithms. The proposed aggregate performance index helps achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate an effectiveness of the introduced model, several numeric data sets are experimented with. Those include a time-series data of gas furnace, NOx emission process of gas turbine power plant and some synthetic data.

Implicit rule-based fuzzy-neural networks using the identification algorithm of GA hybrid scheme based on information granulation

Abstract This paper proposes an identification method for nonlinear models realized in the form of implicit rule-based fuzzy-neural networks (FNN). The design of the model dwells on the technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithm. The FNN modeling and identification environment realizes parameter estimation through a synergistic usage of clustering techniques, genetic optimization and a complex search method. An HCM (Hard C-Means) clustering algorithm helps determine an initial location (parameters) of the membership functions of the information granules to be used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the optimization algorithm of a GA hybrid scheme. The proposed GA hybrid scheme combines GA with the improved complex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) is used in the model design in order to achieve a sound balance between its approximation and generalization abilities. The proposed type of the model is experimented with several time series data (gas furnace, sewage treatment process, and NO x emission process data of gas turbine power plant).

A New Approach to the Development of Genetically Optimized Multilayer Fuzzy Polynomial Neural Networks

In this paper, the authors propose and investigate a new category of neurofuzzy networks-fuzzy polynomial neural networks (FPNNs)-and develop a comprehensive design methodology involving mechanisms of genetic optimization and, in particular, genetic algorithms (GAs). The conventional FPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended group method of data handling, with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The design proposed in this paper addresses this issue. The augmented genetically optimized FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison with the one encountered in the conventional FPNN. The GA-based design procedure that is applied to each layer of FPNN leads to the selection of the preferred nodes (or fuzzy polynomial neurons) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs, whereas the ensuing, detailed parametric optimization is carried out in the setting of a standard least-square-method-based learning. The performance of gFPNN is quantified through experimentation where a number of modeling benchmarks are being used, i.e., synthetic and experimental data already experimented within fuzzy or neurofuzzy modeling. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models

Multi-FNN identification based on HCM clustering and evolutionary fuzzy granulation

Abstract In this paper, we introduce a general category of multi-fuzzy-neural networks (FNNs), analyze their underlying architecture and propose a comprehensive identification framework. The proposed multi-FNNs dwells on a concept of linear fuzzy inference-based FNNs. The design of the model uses a standard HCM (Hard C-Means) clustering algorithm and carries out an evolutionary fuzzy granulation of experimental data. The performance of the model is quantified through a series of experimental studies involving synthetic and real-world data.

GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems

In this paper, we introduce the architecture of Genetic Algorithm (GA) based Feed-forward Polynomial Neural Networks (PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes (PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System (MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

A study on multi-layer fuzzy polynomial inference system based on an extended GMDH algorithm

A new design methodology is proposed to identify the structure and parameters of a fuzzy model using PNN and a fuzzy inference method. The PNN is the extended structure of the GMDH (Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and cubic besides the biquadratic polynomial used in the GMDH. The FPNN (Fuzzy Polynomial Neural Networks) algorithm uses PNN (Polynomial Neural network) structure and the fuzzy inference method. In the fuzzy inference method, the simplified and regression polynomial inference methods are used. Here a regression polynomial inference is based on a consequence of fuzzy rules with polynomial equations such as linear, quadratic and cubic equations. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture. We consider a model that combines the advantage of both FPNN and PNN. Also we use the training and testing data set to obtain a balance between the approximation and generalization of the process model. Several numerical examples are used to evaluate the performance of our proposed model.

Optimization of self-organizing fuzzy polynomial neural networks with the aid of granular computing and evolutionary algorithm

In this study, we introduce and investigate a class of intelligence architectures of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized Fuzzy Polynomial Neurons(FPNs), develop a comprehensive design methodology involving mechanisms of genetic algorithms and information granulation. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of SOFPNN leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network.

A new approach to radial basis function-based polynomial neural networks: analysis and design

In this study, we introduce a new topology of radial basis function-based polynomial neural networks (RPNNs) that is based on a genetically optimized multi-layer perceptron with radial polynomial neurons (RPNs). This paper offers a comprehensive design methodology involving various mechanisms of optimization, especially fuzzy C-means (FCM) clustering and particle swarm optimization (PSO). In contrast to the typical architectures encountered in polynomial neural networks (PNNs), our main objective is to develop a topology and establish a comprehensive design strategy of RPNNs: (a) The architecture of the proposed network consists of radial polynomial neurons (RPN). These neurons are fully reflective of the structure encountered in numeric data, which are granulated with the aid of FCM clustering. RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear polynomial processing. (b) The PSO-based design procedure being applied to each layer of the RPNN leads to the selection of preferred nodes of the network whose local parameters (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, the number of clusters of FCM clustering, and a fuzzification coefficient of the FCM method) are properly adjusted. The performance of the RPNN is quantified through a series of experiments where we use several modeling benchmarks, namely a synthetic three-dimensional data and learning machine data (computer hardware data, abalone data, MPG data, and Boston housing data) already used in neuro-fuzzy modeling. A comparative analysis shows that the proposed RPNN exhibits higher accuracy in comparison with some previous models available in the literature.