Browsing by Author "Su, Miin-Tsair"

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A Hybrid of Bacterial Foraging Optimization and Particle Swarm Optimization for Evolutionary Neural Fuzzy Classifier
(2014-09-01)
Chen, Cheng-Hung
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Su, Miin-Tsair
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Lin, Cheng-Jian
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Lin, Chin-Teng
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交大名義發表
This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method performs local search through the chemotactic movement operation of bacterial foraging whereas the global search over the entire search space is accomplished by a particle swarm operator. The NFC model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. Finally, the proposed neural fuzzy classifier with bacterial foraging particle swarm optimization (NFC-BFPSO) is adopted in several classification applications. Experimental results have demonstrated that the proposed NFC-BFPSO method can outperform other methods.
A Hybridization of Immune Algorithm with Particle Swarm Optimization for Neuro-Fuzzy Classifiers
(2008-09-01)
Lin, Chin-Teng
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Yang, Chien-Ting
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Su, Miin-Tsair
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電控工程研究所
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Institute of Electrical and Control Engineering
In order to enhance the immune algorithm (IA) performance and find the optimal solution when dealing with difficult problems, we propose an efficient immune-based particle swarm optimization (IPSO) for neuro-fuzzy classifiers to solve the skin color detection problem. The proposed IPSO combines the immune algorithm (IA) and particle swarm optimization (PSO) to perform parameter learning. The IA uses the clonal selection principle, such that antibodies between others of high similar degree are affected, and these antibodies, after the process, will have higher quality, accelerating the search and increasing the global search capacity. The PSO algorithm has proved to be very effective for solving global optimization. It is not only a recently invented high-performance optimizer that is easy to understand and implement, but it also requires little computational bookkeeping and generally only a few lines of code. Hence, we employed the advantages of PSO to improve the mutation mechanism of the immune algorithm. Simulations have shown the performance and applicability of the proposed method.
A NOVEL METHOD FOR LOCATING SOLDER JOINTS BASED ON MODIFIED BINARY POTENTIAL FUNCTION
(2012-01-01)
Su, Miin-Tsair
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Lin, Chin-Teng
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Hsu, Keng-Wei
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電機工程學系
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Department of Electrical and Computer Engineering
The solder joint location on inductors is an extremely important aspect of the industrial process; in particular, the visual location plays a fundamental role. The visual-location process is often carried out by human experts. The disadvantages associated with manual location are the large amount of time required and reduced efficiency as operator fatigue occurs. In addition, as electronic products now tend to be miniaturized, portable and with dense component layout, manual location is becoming unreliable. This has prompted the development of automatic visual-location systems to speed up the location process, increase production efficiency and improve manufacture yield rate. In this paper, we propose an automatic visual-location method for solder joints to address the problem of feature extraction in digital images, using the concept of potential functions (PF). In order to make the location method more suitable for the inductor industry, the virtual external electric field concept is introduced. The proposed location system, which uses modified binary potential functions (MBPF), has been implemented and tested with three kinds of inductors. The experimental results show that the proposed scheme performs with a high degree of accuracy, even with testing samples that are significantly different in appearance.
A Rule-Based Symbiotic MOdified Differential Evolution for Self-Organizing Neuro-Fuzzy Systems
(2011-12-01)
Su, Miin-Tsair
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Chen, Cheng-Hung
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Lin, Cheng-Jian
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Lin, Chin-Teng
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電機工程學系
This study proposes a Rule-Based Symbiotic MOdified Differential Evolution (RSMODE) for Self-Organizing Neuro-Fuzzy Systems (SONFS). The RSMODE adopts a multi-subpopulation scheme that uses each individual represents a single fuzzy rule and each individual in each subpopulation evolves separately. The proposed RSMODE learning algorithm consists of structure learning and parameter learning for the SONFS model. The structure learning can determine whether or not to generate a new rule-based subpopulation which satisfies the fuzzy partition of input variables using the entropy measure. The parameter learning combines two strategies including a subpopulation symbiotic evolution and a modified differential evolution. The RSMODE can automatically generate initial subpopulation and each individual in each subpopulation evolves separately using a modified differential evolution. Finally, the proposed method is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed RSMODE learning algorithm. (C) 2011 Elsevier B. V. All rights reserved.
Nonlinear System Control Using Functional-Link-Based Neuro-Fuzzy Network Model Embedded with Modified Particle Swarm Optimizer
(2012-03-01)
Su, Miin-Tsair
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Lin, Chin-Teng
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Hsu, Sheng-Chih
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Li, Dong-Lin
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Lin, Cheng-Jian
This study presents an evolutionary neural fuzzy system (NFS) for nonlinear system control. The proposed NFS model uses functional link neural networks (FLNNs) as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. A learning algorithm, which consists of structure learning and parameter learning, is presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the particle swarm optimization (PSO) algorithm, can adjust the shape of the membership function and the corresponding weighting of the FLNN. The distance-based mutation operator, which strongly encourages a global search giving the particles more chance of converging to the global optimum, is introduced. The simulation results have shown the proposed method can improve the searching ability and is very suitable for the nonlinear system control applications.
SMOKE DETECTION USING SPATIAL AND TEMPORAL ANALYSES
(2012-07-01)
Lee, Chen-Yu
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Lin, Chin-Teng
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Hong, Chao-Ting
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Su, Miin-Tsair
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電機工程學系
"Video-based fire detection is currently a fairly common application with the growth in the number of installed surveillance video systems. Moreover, the related processing units are becoming more powerful. Smoke is an early sign of most fires; therefore, selecting an appropriate smoke-detection method is essential. However, detecting smoke without creating a false alarm remains a challenging problem for open or large spaces with the disturbances of common moving objects, such as pedestrians and vehicles. This study proposes a novel video-based smoke-detection method that can be incorporated into a surveillance system to provide early alerts. In this study, the process of extracting smoke features from candidate regions was accomplished by analyzing the spatial and temporal characteristics of video sequences for three important features: edge blurring, gradual energy changes, and gradual chromatic configuration changes. The proposed spatial-temporal analysis technique improves the feature extraction of gradual energy changes. In order to make the video smoke-detection results more reliable, these three features Were combined using a support vector machine (SVM) technique and a temporal-based alarm decision unit (AD U) was also introduced. The effectiveness of the proposed algorithm was evaluated on a PC with an Intel (R) Core (TM) 2 Duo CPU (2.2 GHz) and 2 GB RAM. The average processing time was 32.27 ms per frame; i.e., the proposed algorithm can process 30.98 frames per second. Experimental results showed that the proposed system can detect smoke effectively with a low false-alarm rate and a short reaction time in many real-world scenarios."