Yu-Chuan Chang

Fuzzy Interpolative Reasoning for Sparse Fuzzy-Rule-Based Systems Based on the Areas of Fuzzy Sets

Fuzzy interpolative reasoning is an inference technique for dealing with the sparse rules problem in sparse fuzzy-rule-based systems. In this paper, we present a new fuzzy interpolative reasoning method for sparse fuzzy-rule-based systems based on the areas of fuzzy sets. The proposed method uses the weighted average method to infer the fuzzy interpolative reasoning results and has the following advantages: (1) it holds the normality and the convexity of the fuzzy interpolative reasoning result, (2) it can deal with fuzzy interpolative reasoning with complicated membership functions, (3) it can deal with fuzzy interpolative reasoning when the fuzzy sets of the antecedents and the consequents of the fuzzy rules have different kinds of membership functions, (4) it can handle fuzzy interpolative reasoning with multiple antecedent variables, (5) it can handle fuzzy interpolative reasoning with multiple fuzzy rules, and (6) it can handle fuzzy interpolative reasoning with logically consistent properties with respect to the ratios of fuzziness. We use some examples to compare the fuzzy interpolative reasoning results of the proposed method with those of the existing fuzzy interpolative reasoning methods. In terms of the six evaluation indices, the experimental results show that the proposed method performs more reasonably than the existing methods. The proposed method provides us a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy-rule-based systems.

Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques

In this paper, we present a new method for multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. First, the proposed method constructs training samples based on the variation rates of the training data set and then uses the training samples to construct fuzzy rules by making use of the fuzzy C-means clustering algorithm, where each fuzzy rule corresponds to a given cluster. Then, we determine the weight of each fuzzy rule with respect to the input observations and use such weights to determine the predicted output, based on the multiple fuzzy rules interpolation scheme. We apply the proposed method to the temperature prediction problem and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) data. The experimental results show that the proposed method produces better forecasting results than several existing methods.

Fuzzy Rules Interpolation for Sparse Fuzzy Rule-Based Systems Based on Interval Type-2 Gaussian Fuzzy Sets and Genetic Algorithms

In this paper, we present a new method for fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms. First, we present a method to deal with the interpolation of fuzzy rules based on interval type-2 Gaussian fuzzy sets. We also prove that the proposed method guarantees to produce normal interval type-2 Gaussian fuzzy sets. Then, we present a method to learn optimal interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. We also apply the proposed fuzzy rules interpolation method and the proposed learning method to deal with multivariate regression problems and time series prediction problems. The experimental results show that the proposed fuzzy rules interpolation method using the optimally learned interval type-2 Gaussian fuzzy sets gets higher average accuracy rates than the existing methods.

Weighted Fuzzy Rule Interpolation Based on GA-Based Weight-Learning Techniques

In this paper, we propose a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. It is based on a genetic algorithm (GA)-based weight-learning technique. The proposed method can deal with fuzzy rule interpolation with weighted antecedent variables. It also can deal with fuzzy rule interpolation based on polygonal membership functions and bell-shaped membership functions. We also propose a GA-based weight-learning algorithm to automatically learn the optimal weights of the antecedent variables of the fuzzy rules. Furthermore, we apply the proposed weighted fuzzy interpolative reasoning method and the proposed GA-based weight-learning algorithm to deal with the truck backer-upper control problem, the computer activity prediction problem, multivariate regression problems, and time series prediction problems. Based on statistical analysis techniques, the experimental results show that the proposed weighted fuzzy interpolative reasoning method by the use of the optimally learned weights that were obtained by the proposed GA-based weight-learning algorithm has statistically significantly smaller error rates than the existing methods.

Weighted Fuzzy Interpolative Reasoning Based on Weighted Increment Transformation and Weighted Ratio Transformation Techniques

In this paper, we present a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. The proposed method uses weighted increment transformation and weighted ratio transformation techniques to handle weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. It allows each variable that appears in the antecedent parts of fuzzy rules to associate with a weight between zero and one. Moreover, we also propose an algorithm that automatically tunes the optimal weights of the antecedent variables appearing in the antecedent parts of fuzzy rules. We also apply the proposed weighted fuzzy interpolative reasoning method to handle the truck backer-upper control problem. The proposed weighted fuzzy interpolative reasoning method performs better than the ones obtained by the traditional fuzzy inference system (2000), Huang and Shens method (2008), and Chen and Kos method (2008). The proposed method provides us with a useful way to deal with weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

A new query reweighting method for document retrieval based on genetic algorithms

In this paper, we present a new method for query reweighting to deal with document retrieval. The proposed method uses genetic algorithms to reweight a users query vector, based on the users relevance feedback, to improve the performance of document retrieval systems. It encodes a users query vector into chromosomes and searches for the optimal weights of query terms for retrieving documents by genetic algorithms. After the best chromosome is found, the proposed method decodes the chromosome into the users query vector for dealing with document retrieval. The proposed query reweighting method can find the best weights of query terms in the users query vector, based on the users relevance feedback. It can increase the precision rate and the recall rate of the document retrieval system for dealing with document retrieval

Fuzzy rule interpolation based on the ratio of fuzziness of interval type-2 fuzzy sets

In recent years, some fuzzy rule interpolation methods have been presented for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. However, the existing methods have the drawbacks that they cannot guarantee the convexity of the fuzzy interpolated result and may generate the same fuzzy interpolated results with respect to different observations. Moreover, they also cannot deal with fuzzy rule interpolation with bell-shaped interval type-2 fuzzy sets. In this paper, we present a new method for fuzzy rule interpolation for sparse fuzzy rule-based systems based on the ratio of fuzziness of interval type-2 fuzzy sets. The proposed method can overcome the drawbacks of the existing methods. First, it calculates the weights of the closest fuzzy rules with respect to the observation to obtain an intermediate consequence fuzzy set. Then, it uses the ratio of fuzziness of interval type-2 fuzzy sets to infer the fuzzy interpolated result based on the intermediate consequence fuzzy set. We also use some examples to compare the fuzzy interpolated results of the proposed method with the results by the existing methods. The experimental results show that the proposed fuzzy rule interpolation method gets more reasonable results than the existing methods.

Multilabel text categorization based on a new linear classifier learning method and a category-sensitive refinement method

In this paper, we present a new approach for dealing with multilabel text categorization based on a new linear classifier learning method and a category-sensitive refinement method. We use a new weighted indexing technique to construct a multilabel linear classifier. We use the degrees of similarity between categories to adjust the relevance scores of categories with respect to a testing document. The testing document can be properly classified into multiple categories by using a predefined threshold value. We also compare the performance of the proposed method with the text categorization methods based on the Reuters-21578 ModeApte Split Text Collection. The experimental results show that the performance of the proposed method is better than the existing methods.

A new method for multiple fuzzy rules interpolation with weighted antecedent variables

Fuzzy rule interpolation techniques have been used to handle the problems of sparse fuzzy rule bases in sparse fuzzy rule-based systems. In the existing fuzzy rule interpolation methods, there are many variables in the antecedents of fuzzy rules, where the variables in the antecedents of fuzzy rules have the same weight. If we can handle fuzzy rule interpolation with weighted antecedent variables, then there is room for more flexibility. In this paper, we present a new method for multiple fuzzy rules interpolation with weighted antecedent variables. The proposed method not only can handle fuzzy rule interpolation with polygonal membership functions, but also can preserve the convexity of fuzzy interpolative reasoning results. The fuzzy interpolative reasoning results of the proposed method also satisfy the logically consistency with respect to the ratios of fuzziness. The experimental result shows that the proposed method can generate reasonable fuzzy interpolative reasoning results for sparse fuzzy rule-based systems with weighted antecedent variables. The proposed method provides us a useful way for fuzzy rule interpolation in sparse fuzzy rule-based systems with weighted antecedent variables.

A new fuzzy interpolative reasoning method based on interval type-2 fuzzy sets

Fuzzy rule interpolation plays an important role in sparse fuzzy rule-based systems. In this paper, we present a new method for handling fuzzy rule interpolation in sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. The proposed method handles fuzzy rule interpolation based on the principle membership functions and the uncertainty grade functions of interval type-2 fuzzy sets. The proposed method can handle fuzzy rule interpolation with polygonal interval type-2 fuzzy sets. It also can handle fuzzy rule interpolation with multiple antecedent variables and can generate reasonable fuzzy interpolative reasoning results in sparse fuzzy rule-based systems based on interval type-2 fuzzy sets.