Kyung-Won Jang

The Design of Pattern Classification based on Fuzzy Combined Polynomial Neural Network

In this paper, we propose a fuzzy combined Polynomial Neural Network(PNN) for pattern classification. The fuzzy combined PNN comes from the generic TSK fuzzy model with several linear polynomial as the consequent part and is the expanded version of the fuzzy model. The proposed pattern classifier has the polynomial neural networks as the consequent part, instead of the general linear polynomial. PNNs are implemented by stacking the simple polynomials dynamically. To implement one layer of PNNs, the various types of simple polynomials are used so that PNNs have flexibility and versatility. Although the structural complexity of the implemented PNNs is high, the PNNs become a high order-multi input polynomial finally. To estimate the coefficients of a polynomial neuron, The weighted linear discriminant analysis. The output of fuzzy rule system with PNNs as the consequent part is the linear combination of the output of several PNNs. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.

Evolutionary design of Self-Organizing Fuzzy Polynomial Neural Networks for modeling and prediction of NOx emission process

In this study, we proposed genetically dynamic optimized self-organizing fuzzy polynomial neural network with information granulation based FPNs (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. We illustrate the performance of the network and elaborate on its development by experimenting with data coming from the NOx emission process of a gas turbine power plant. The proposed gdSOFPNN gives rise to a structurally and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling

Design methodology of optimized IG_gHSOFPNN and its application to pH neutralization process

In this paper, we propose design methodology of optimized Information granulation based genetically optimized Hybrid Self-Organizing Fuzzy Polynomial Neural Networks (IG_gHSOFPNN) by evolutionary optimization. The augmented IG_gHSOFPNN results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HSOFPNN. The GA-based design procedure being applied at each layer of IG_gHSOFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HSOFPNN. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models.

Genetically optimized fuzzy set-based polynomial neural networks based on information granules with aids of symbolic genetic algorithms

In this paper, we propose a new architecture of Fuzzy Set–based Polynomial Neural Networks (FSPNN) with a new fuzzy set-based polynomial neuron (FSPN) whose fuzzy rules include the information granules (about the real system) obtained through Information Granulation. Although the conventional FPNN with Fuzzy Relation-based Polynomial Neurons has good approximation ability and generalization capability, there is an important drawback that FPNN is very complicated. If we adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules, we can get an advantage of the rule reduction. We use FSPN as a node of Fuzzy Polynomial Neural Networks to reduce the complexity of the FPNN. The proposed FPNN with Fuzzy Set-based Polynomial Neuron can achieve compactness. Information Granulation can extract good information from numerical data without experts knowledge which is important for building Fuzzy Inference System. We put Information Granulation to the proposed FSPN. The structure of the proposed FPNN with FSPN is determined with aids of symbolic gene type genetic algorithms.

A New Fuzzy Inference System with the aid of SAHN based algorithm

In this paper, we have presented a sequential agglomerative hierarchical nested (SAHN) algorithm based data clustering method in fuzzy inference system to achieve optimal performance of fuzzy model. SAHN based algorithm is used to give possible range of number of clusters with cluster centers for the system identification. The axes of membership functions of this fuzzy model are optimized by using cluster centers obtained from clustering method and the consequence parameters of the fuzzy model are identified by standard least square method. Finally, in this paper, we have observed our models output performance using the Box of Jenkinss gas furnace data and Sugenos non-linear process data.

Optimal Fuzzy Models with the Aid of SAHN-based Algorithm

In this paper, we have presented a Sequential Agglomerative Hierarchical Nested (SAHN) algorithm-based data clustering method in fuzzy inference system to achieve optimal performance of fuzzy model. SAHN-based algorithm is used to give possible range of number of clusters with cluster centers for the system identification. The axes of membership functions of this fuzzy model are optimized by using cluster centers obtained from clustering method and the consequence parameters of the fuzzy model are identified by standard least square method. Finally, in this paper, we have observed our models output performance using the Box and Jenkinss gas furnace data and Sugenos non-linear process data.

Data Pattern Estimation with Movement of the Center of Gravity

In the rule based modeling, data partitioning plays crucial role because partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkinss gas furnace data

Computer color management of dyestuff manufacturing on fuzzy inference

A compromised color management method was proposed that was modeled after visual perception instead of conventional color management method that using spectrum analysis. The proposed scheme used a computer color scanner to obtain a bit map image from dyed original color sample, and conduct pixel analysis using an image histogram. From the image histogram, it can extract the color and RGB value that shows the dominant pixel distribution of the sample image. An RGB color model was constructed for dyestuff manufacturing with a rule-based algorithm. Constructed color model can minimize additional experiment to obtain more color and produce the color recipe information from basic data. And performance of color matching was measured as a Euclidean distance that compares the RGB value of the original color with RGB value of the produced color. The matching range was confirmed by threshold of Euclidean distance.