Browsing by Author "Chen, C. L. Philip"

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A New Learning Algorithm for a Fully Connected Neuro-Fuzzy Inference System
(2014-10-01)
Chen, C. L. Philip
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Wang, Jing
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Wang, Chi-Hsu
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Chen, Long
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電機資訊學士班
A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.
Finding the Capacity of Fuzzy Neural Networks (FNNs) via Its Equivalent Fully Connected Neural Networks (FFNNs)
(2011)
Wang, Jing
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Wang, Chi-Hsu
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Chen, C. L. Philip
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
The capacity of Fuzzy Neural Network (FNN) is explored in this paper. The FNN is first transformed into an equivalent fully connected three layer neural network, or FFNN, via a new approach proposed in this paper. Then the lower and upper bounds of FNN can be found. To check the validity of the theoretical bounds, an example is illustrated with the trainings to yield excellent capacity matching with the theoretical bounds. This new finding has its emerging values in all engineering applications using FNN, such as intelligent adaptive control, pattern recognition, and signal processing, ... , etc.
On the BP Training Algorithm of Fuzzy Neural Networks (FNNs) via Its Equivalent Fully Connected Neural Networks (FFNNs)
(2011)
Wang, Jing
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Wang, Chi-Hsu
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Chen, C. L. Philip
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電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
In this paper, Fuzzy Neural Network (FNN) is first transformed into an equivalent fully connected three layer neural network, or FFNN. Based on the FFNN, BP training algorithm is derived to tune both the premise and consequent part of FNN. Illustrative examples are presented to check the validity of the proposed theory and algorithms. Simulation achieves satisfactory results. Developing BP training algorithm for FNN via its equivalent FFNN has its emerging values in all engineering applications using FNN, such as intelligent adaptive control, pattern recognition, and signal processing..., etc
On the Classification of Cancer Cell Gene via Expressive Value Distance (EVD) Algorithm and Its Comparison to the Optimally Trained ANN Method
(2011)
Zhang, Tong
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Tam, Sik Chung
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Wang, Chi-Hsu
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Chen, C. L. Philip
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電機工程學系
In recent years, cancer can be detected and recognized by analyzing the sample\'s expression profile. The cancer gene expression data are high dimensional, high variable dependent, and very noisy. The dimension reduction method is often used for processing the high dimensional data. In this study, a new statistical dimension reduction method called Expressive Value Distance (EVD) is developed and proposed for the practical high-dimensional gene expression cancer data. The feature genes data extracted by EVD are arranged for training the optimally trained Artificial Neural Network (ANN). The trained ANN is then used to classify whether the unseen gene data is cancer or not. In comparison of ANN classification with and without EVD, it is found that both of the ANN can classify the cancer data in good accuracy. With the EVD method, the great amount of data (2000 genes) can be effectively reduced to 16 genes. Therefore, EVD is an effective dimension reduction method. Even the EVD method is not used, the optimally trained ANN is also an advanced method for classifying the high dimensional and complicated cancer data. Briefly, it proves that optimally trained ANN is a very robust classification technique.
On the Conjugate Gradients (CG) Training Algorithm of Fuzzy Neural Networks (FNNs) via Its Equivalent Fully Connected Neural Networks (FFNNs)
(2012-01-01)
Wang, Jing
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Chen, C. L. Philip
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Wang, Chi-Hsu
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電機工程學系
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Department of Electrical and Computer Engineering
In this paper, Fuzzy Neural Network (FNN) is transformed into an equivalent fully connected three layer neural network, or FFNN. Based on the FFNN, conjugate gradients (CG) training algorithm is derived to tune both the premise and consequent part of FNN, and apparently increase the speed of convergence. Illustrative examples are presented to check the validity of the proposed theory and algorithms. Simulation achieves satisfactory results. Developing CG training algorithm for FNN via its equivalent FFNN has its emerging values in all engineering applications using FNN, such as intelligent adaptive control, pattern recognition, and signal processing., etc
The Bounded Capacity of Fuzzy Neural Networks (FNNs) Via a New Fully Connected Neural Fuzzy Inference System (F-CONFIS) With Its Applications
(2014-12-01)
Wang, Jing
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Wang, Chi-Hsu
;
Chen, C. L. Philip
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
In this paper, a fuzzy neural network (FNN) is transformed into an equivalent three-layer fully connected neural inference system (F-CONFIS). This F-CONFIS is a new type of a neural network whose links are with dependent and repeated weights between the input layer and hidden layer. For these special dependent repeated links of the F-CONFIS, some special properties are revealed. A new learning algorithm with these special properties is proposed in this paper for the F-CONFIS. The F-CONFIS is therefore applied for finding the capacity of the FNN. The lower bound and upper bound of the capacity of the FNN can be found from a new theorem proposed in this paper. Several examples are illustrated with satisfactory simulation results for the capacity of the F-CONFIS (or the FNN). These include "within capacity training of the FNN," "over capacity training of the FNN," "training by increasing the capacity of the FNN," and "impact of the capacity of the FNN in clustering Iris Data." It is noted that the finding of the capacity of the F-CONFIS, or FNN, has its emerging values in all engineering applications using fuzzy neural networks. This is to say that all engineering applications using FNN should not exceed the capacity of the FNN to avoid unexpected results. The clustering of Iris data using FNN illustrated in this paper is one of the most relevant engineering applications in this regards.