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Soft-Boosted Self-Constructing Neural Fuzzy Inference Network
(2017-03)
Prasad, Mukesh
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Lin, Chin-Teng
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Li, Dong-Lin
;
Hong, Chao-Tien
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Ding, Wei-Ping
This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets.
Self-adjusting feature maps network and its applications
(2016-09-26)
Li, Dong-Lin
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Prasad, Mukesh
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Lin, Chin-Teng
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Chang, Jyh-Yeong
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資訊工程學系
This paper, proposes a novel artificial neural network, called self-adjusting feature map (SAM), and develop its unsupervised learning ability with self-adjusting mechanism. The trained network structure of representative connected neurons not only displays the spatial relation of the input data distribution but also quantizes the data well. The SAM can automatically isolate a set of connected neurons, in which, the used number of the sets may indicate the number of clusters. The idea of self-adjusting mechanism is based on combining of mathematical statistics and neurological advantages and retreat of waste. In the training process,, for each representative neuron has are three phases, growth, adaptation and decline. The network of representative neurons, first create the necessary neurons according to the local density of the input data in the growth phase. In the adaption phase, it adjusts neighborhood neuron pair\'s connected/disconnected topology constantly according to the statistics of input feature data. Finally, the unnecessary neurons of the network are merged or remove in the decline phase. In this paper, we exploit the SAM to handle some peculiar cases that cannot be handled easily by classical unsupervised learning networks such as self-organizing map (SOM) network. The remarkable characteristics of the SAM can be seen on various real world cases in the experimental results. (C) 2016 Elsevier B.V. All rights reserved.
APPLYING GENERALIZED WEIGHTED MEAN AGGREGATION TO IMPULSIVE NOISE REMOVAL OF IMAGES
(2014)
Chen, Kuan-Lin
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Chang, Jyh-Yeong
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電機工程學系
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Department of Electrical and Computer Engineering
In this paper, we apply generalized weighted mean to construct interval-valued fuzzy relations for grayscale image impulse noise detection and correction. First, we employ two weighting parameters and perform the weighted mean aggregation for the central pixel and its eight neighbor pixels in a 3x3 sliding window across the image. Then, to counter the over-weighting of a big difference term, we apply a saturation threshold transfer function to these eight pixel difference values. Finally, the image noise map is obtained through a threshold operation on the cumulative differences. To decrease the noise detection error, weighting parameters of the mean can be learned by the gradient method caste in discrete formulation. Moreover, to get higher PSNR in the corrected image, we have experienced from the training that we will select weight of 20 for noise rate smaller than 20% and 50 for noise rate greater than 20%, on erroneous noisy than that on the erroneous non-noise pixel. By the experiment, we have shown that the integration of interval-valued fuzzy relations with the weighted mean aggregation algorithm can effectively detect the image noise pixels and then correct them thereafter.
Similarity Based ART 1 Model for Automatic Die Defect Detection and Classification
(2014)
Chang, Jyh-Yeong
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Chang, Wei-Lun
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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
In general, a reliable and automatic semiconductor fabrication processes is of great importance to product yield and cost reduction. In the past, we made use of human vision to do wafer die defect detection and classification, which is hindered by the easy fatigue and fuzziness of human eyes and the decision difference between inspectors. In this paper, we develop a vision-based automatic defect classification system. In our defect detection component, we apply the MAD method to align the test image with the reference image. To acquire the binary defect images, we subtract the test image from the reference image, then we convert the difference image into the binary image by setting a threshold. Moreover, we removed the scattering noises by setting a minimum number of connected noisy pixels required. Finally, we extract all defects in the test image in order to perform the defect classification. For defect classification, we revise the ART 1 model, which still can retain its stability and the plasticity dilemma. We have found that ART 1 shows an intolerable shortcoming: whose output is dependent on the ordering of input sequence applied to the ART 1 model. To remedy this disadvantage, we derive the similarity based ART 1 model which can obtain high classification accuracy and independent on the ordering of input patterns applied.
A Fuzzy Weighted Mean Aggregation Algorithm for Color Image Impulse Noise Removal
(2015)
Chang, Jyh-Yeong
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Liu, Pin-Chang
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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
In this paper, we utilize fuzzy weighted mean aggregation algorithm to construct Interval-Valued Fuzzy Relations (IVFR) for grayscale image noise detection. To this end, we use two weighting parameters to calculate the weighted mean difference of the central pixel and its 8-neighborhood pixels in a sliding window across the image. Then, the central pixel will be identified as noisy or non-noisy by using a threshold operation. Besides, to decrease the noise pixel detection error, we have derived an iterative learning mechanism of these weighting parameters of the mean aggregation and thresholds in the training stage. Finally, we embed the pocket algorithm in our learning mechanism to train the best parameter set to minimize the noisy and noise free pixel detection error. The flexibility of the proposed IVFR approach is quite suited to learn the characteristics existing among the noisy pixel and its neighbors. Thus the derived IVPR scheme can excellently detect a noisy pixel and lead to marvelous result on impulsive noise removal. In the pixel restoration stage, we propose a new filtering method. It is divided into three steps: image histogram, noise detection, and image restoration. First, we calculate the histogram of the testing image to find the groups of potential noise pixels. On these possible noisy pixel groups, we make use of the trained weighting parameters to do the fuzzy weighted mean aggregation to double-check whether they are noise corrupted or not. If a pixel is identified as noisy, its value will be restored by a weighted mean filter. Simulation results show that the proposed algorithm provides a significant improvement over other existing filters and preserves more image details. Our algorithm can barely restore the image even when the noise rate is as high as 97 %.
Day-and-Night Video Based Face Identification
(2015)
Chang, Jyh-Yeong
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Chan, Tzu-Rou
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Fu, Hsin-Chia
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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
Human face recognition system is a desired technique in our daily life. It is a widely well-come technique that can all-day-long and on-line recognize a person from video cameras. To this end, we use a near infrared (NIR) camera to capture day-and-night video images for on-line human recognition. In this paper, we adopt human face sub-image attraction package in OpenCV, which is based on Haar cascade classifier. The package is a feature-based algorithm and works much faster than the pixel-based algorithm. It is to be noted that the image contrast color tones of video frames in the night is worse than that in the day, thus we employ multi-scale retinex to enhance video frames in the night before OpenCV face extraction routine. The extracted face sub-image is first transformed to a new space by eigenspace and canonical space transformation. The recognition is finally done in canonical space. Despite OpenCV\'s popularity to date, extracting face sub-images from taken videos are still not reliable enough. Namely, we can obtain many non-face sub-images among the extracted face sub-images. We judiciously classify the sub-images that are far away from the centroids of persons to be classified as non-face sub-images. This may remedy the shortcoming of OpenCV package, and greatly increase the face recognition accuracy. Furthermore, we consider the most recent three consecutive face image recognitions from video, and use majority vote to recognize a person to enhance the accuracy. Besides, we have tested face image recognition to reject intruders successfully.
A Hybrid of Cuckoo Search and Simplex Method for Fuzzy Neural Network Training
(2015)
Chang, Jyh-Yeong
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Liao, Shih-Hui
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Wu, Shang-Lin
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Lin, Chin-Teng
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電控工程研究所
In this paper, a new hybrid algorithm mixing the simplex method of Nelder and Mead (NM) and the cuckoo search (CS), abbreviated as NM-CS, is proposed for the training of the Fuzzy Neural Networks (FNNs). In standard CS, cuckoo birds engage the obligate brood parasitism by laying their own eggs to other host birds. If a host bird discovers the alien eggs, they will either throw these eggs away or abandon its nest and build a new nest elsewhere. In the proposed hybrid algorithm, instead of using the probability to discover an alien egg for the CS, we use the concept of a simplex which is used in the NM algorithm to abandon and generate the new nests. Our proposed method puts more emphasis on exploration of the search space and enhances the ability to avoid local optimum. Some simulation problems will be provided to compare the performances of the proposed method and other methods in training an FNN. In these simulations, it is observed that the proposed method outperforms other methods.
Optimization of Fuzzy Systems Using Group-Based Evolutionary Algorithm
(2012-01-01)
Chang, Jyh-Yeong
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Han, Ming-Feng
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Lin, Chin-Teng
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電控工程研究所
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Institute of Electrical and Control Engineering
This paper proposes a group-based evolutionary algorithm (GEA) for the fuzzy system (FS) optimization. Initially, we adopt an entropy measure method to determine the number of rules. Fuzzy rules are automatically generated from training data by entropy measure. Subsequently, the GEA is performed to optimize all the free parameters for the FS design. In the evolution process, a FS is coded as an individual. All individuals based on their performance are partitioned into a superior group and an inferior group. The superior group, which is composed of individuals with better performance, uses a global evolution operation to search potential individuals. In the inferior group, individuals with a worse performance employ the local evolution operation to search better individuals near the current best individual. Finally, the proposed FS with GEA model (FS-GEA) is applied to time series forecasting problem. Results show that the proposed FS-GEA model obtains better performance than other algorithm.
Training neural networks via simplified hybrid algorithm mixing Nelder-Mead and particle swarm optimization methods
(2015-03-01)
Liao, Shih-Hui
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Hsieh, Jer-Guang
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Chang, Jyh-Yeong
;
Lin, Chin-Teng
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電控工程研究所
In this paper, a new and simplified hybrid algorithm mixing the simplex method of Nelder and Mead (NM) and particle swarm optimization algorithm (PSO), abbreviated as SNM-PSO, is proposed for the training of the parameters of the Artificial Neural Network (ANN). Our method differs from other hybrid PSO methods in that, n+1 particles, where n is the dimension of the search space, are randomly selected (without sorting), at each iteration of the proposed algorithm for use as the initial vertices of the NM algorithm, and each such particle is replaced by the corresponding final vertex after executing the NM algorithm. All the particles are then updated using the standard PSO algorithm. Our proposed method is simpler than other similar hybrid PSO methods and places more emphasis on the exploration of the search space. Some simulation problems will be provided to compare the performances of the proposed method with PSO and other similar hybrid PSO methods in training an ANN. These simulations show that the proposed method outperforms the other compared methods.
Fuzzy k-nearest neighbor classifier to predict protein solvent accessibility
(2008)
Chang, Jyh-Yeong
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Shyu, Jia-Jie
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Shi, Yi-Xiang
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電控工程研究所
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Institute of Electrical and Control Engineering
The prediction of protein solvent accessibility is an intermediate step for predicting the tertiary structure of proteins. Knowledge of solvent accessibility has proved useful for identifying protein function, sequence motifs, and domains. Using a position-specific scoring matrix (PSSM) generated from PSI-BLAST in this paper, we develop the modified fuzzy k-nearest neighbor method to predict the protein relative solvent accessibility. By modifying the membership functions of the fuzzy k-nearest neighbor method by Sim et al. [1], has recently been applied to protein solvent accessibility prediction with excellent results. Our modified fuzzy k-nearest neighbor method is applied on the three-state, E, I, and B, and two-state, E, and B, relative solvent accessibility predictions, and its prediction accuracy compares favorly with those by the fuzzy k-NN and other approaches.

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