Summary: | 博士 === 國立交通大學 === 電機與控制工程系 === 88 === This thesis presents two new integrated fuzzy neural approaches for classifier design and image segmentation processing.
First, we propose a novel learning algorithm of Fuzzy Perceptron Neural Networks (FPNN) for classifiers that utilize expert knowledge represented by fuzzy if-then rules as well as numerical data as inputs. The conventional linear perceptron network is extended to a second-order one, which is much more flexible for defining a discriminant function. In order to handle fuzzy numbers in neural networks, level sets of fuzzy input vectors are incorporated into perceptron neural learning. At different levels of the input fuzzy numbers, updating the weight vector depends on the minimum of the output of the fuzzy perceptron neural network and the corresponding nonfuzzy target output that indicates the correct class of the fuzzy input vector. This minimum is computed efficiently by employing the modified vertex method to lessen the computational load and the training time required. Moreover, the pocket algorithm is introduced into our fuzzy perceptron learning scheme, called fuzzy pocket algorithm, to solve the nonseparable problems. Simulation results demonstrate the effectiveness of the proposed FPNN model.
Second, in this thesis, we propose a fuzzy-logic-based modified single-layer perceptron (MSLP) image segmentation network for object extraction. We select a sigmoid gray level transfer function with the aid of the input image histogram and map the input gray levels into the interval [0,1]. Then we adopt the linear index of fuzziness of the output nodes as the error function of the image segmentation system to incorporate the learning capability of a neural network. Our scheme can successfully extract objects from the background. To further enhance the capability of the segmentation system, the proposed network is incorporated with fuzzy if-then rules to adaptively adjust the threshold of the activation function of the MSLP output neuron for best matching the local characteristics of the image. Fuzzy if-then rules involving the edge intensities and vertical positions of pixels are reasoned to determine the threshold adaptively. From the results of segmenting forward looking infrared (FLIR) images, better segmentation images have been obtained by incorporating fuzzy if-then rules with the MSLP segmentation technique.
As demonstrated in this study, it is promising and worthwhile to incorporate human knowledge in terms of fuzzy logic into a designed numerical algorithm, which can further improve the performance, not just for the classification or segmentation problem we have presented.
1.1 Overview
1.2 The Neuro-Fuzzy Approaches for Solving Problems
1.2.1 Fuzzy Perceptron Learning and Its application to Classifier
1.2.2 A Fuzzy Logic-Based Neural Network for Image Segmentation Problem
1.3 Thesis Outline
2. LITERATURE SURVEY
3. THE FUZZY PERCEPTRON NEURAL NETWORK
3.1 Fuzzy Function and The Extension Principle
3.2 Structure of the Fuzzy Perceptron Neural Network
3.3 Fuzzy Perceptron Learning by the Modified Vertex Method
3.4 Fuzzy Pocket Algorithm
3.5 Multiclass Classification
3.6 Simulation
3.6.1 Simulation 1
3.6.2 Simulation 2
4. APPLYING FUZZY LOGIC IN THE MODIFIED SINGLE-LAYER PERCEPTRON IMAGE SEGMENTATION NETWORK
4.1 Single-layer Perceptron Net and Delta Learning Rule
4.2 The Modified Single-layer Perceptron Segmentation Network for Object Extraction
4.2.1 The MSLP Network Architecture
4.2.2 Sigmoid Gray Level Transfer Function of the Input Image Pixels
4.2.3 The Learning Procedures of the MSLP Segmentation Network
4.2.4 Simulation Results
4.3 Incorporating Fuzzy If-then Rules With The Modified Single-layer Perceptron Network
4.3.1 Fuzzy Reasoning in Adjusting the Threshold of Output Neurons
4.3.2 Simulation Results
5. CONCLUSION
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