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Supervised and Reinforcement Group-Based Hybrid Learning Algorithms for TSK-type Fuzzy Cerebellar Model Articulation Controller
(2019-01-01)
Jhang, Jyun-Yu
;
Lin, Cheng-Jian
;
Li, Lingling
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this study, a Takagi-Sugeno-Kang (TSK)-type fuzzy cerebellar model articulation controller (T-FCMAC) based on a group-based hybrid learning algorithm (GHLA) was proposed for solving various problems. The proposed T-FCMAC model was mainly derived from a traditional cerebellar model articulation controller and the TSK-type fuzzy model. For supervised learning, the proposed GHLA was developed by combining an improved quantum particle swarm optimization algorithm and the Nelder-Mead method for adjusting the parameters of a T-FCMAC. The fuzzy C-mean clustering technique was adopted to improve the performance of quantum particle swarm optimization. A fitness threshold was used to determine the number of clusters in fuzzy C-mean clustering. The grouping concept was also used to improve the search ability and increase the convergence rate. Moreover, because exact training data may be expensive or even impossible to obtain in some real-world applications, a reinforcement GHLA (R-GHLA) was proposed. Experimental results revealed the performance and applicability of the proposed GHLA and R-GHLA.
Navigation Control of Mobile Robots Using an Interval Type-2 Fuzzy Controller Based on Dynamic-group Particle Swarm Optimization
(2018-10-01)
Jhang, Jyun-Yu
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Lin, Cheng-Jian
;
Lin, Chin-Teng
;
Young, Kuu-Young
;
電機工程學系
This paper presents an effective navigation control method for mobile robots in an unknown environment. The proposed behavior manager (BM) switches between two behavioral control patterns, wall-following behavior (WFB) and toward-goal behavior (TGB), based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller with a dynamic-group particle swarm optimization (DGPSO) algorithm is proposed to provide WFB control and obstacle avoidance for mobile robots. In the WFB learning process, the input signal of a controller is the distance between the wall and the sonar sensors, and its output signal is the speed of two wheels of a mobile robot. A fitness function, which operates on the total distance traveled by the mobile robot, distance from the side wall, angle to the side wall, and moving speed, evaluates the WFB performance of the mobile robot. In addition, an escape mechanism is proposed to avoid a dead cycle. Experimental results reveal that the proposed DGPSO is superior to other methods in WFB and navigation control.
Particle Swarm Optimization Based Feature Selection in Mammogram Mass Classification
(2012)
Wong, Man To
;
He, Xiangjian
;
Hung Nguyen
;
Yeh, Wei-Chang
;
工業工程與管理學系
Mammography is currently the most effective method for early detection of breast cancer. This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal tissue regions by first finding the significant texture features of ROI using binary particle swarm optimization (BPSO). The data set used consisted of sixty-nine ROIs from the MIAS Mini-Mammographic database. Eighteen texture features were derived from the gray level co-occurrence matrix (GLCM) of each ROI. Significant features are found by a feature selection technique based on BPSO. The decision tree classifier is then used to classify the test set using these significant features. Experimental results show that the significant texture features found by the BPSO based feature selection technique can have better classification accuracy when compared to the full set of features. The BPSO feature selection technique also has similar or better performance in classification accuracy when compared to other widely used existing techniques.
Wireless Mesh Router Placement with Constraints of Gateway Positions and QoS
(2015)
Lin, Chun-Cheng
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Chen, Tung-Huei
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Jhong, Shun-Yu
;
工業工程與管理學系
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Department of Industrial Engineering and Management
The past studies on router node placement for wireless mesh networks (WMNs) did not consider placement of Internet gateways. Therefore, mesh routers and mesh clients can only communicate locally. The problem in this paper is to maximize both network connectivity and client coverage for the router node placement in WMNs consisting of mesh routers, mesh clients, and Internet gateways, subject to three QoS constraints: delay, relay load, and Internet gateway capacity. By visualizing the placements in previous works, we discover two main drawbacks: overlapping and coverless. To solve them, this paper presents a novel particle swarm optimization approach. Performance of the proposed approach is verified by simulation.
Applying Hueristic Algorithms to Portfolio Selection Problem
(2015)
Kang, Po-Ya
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Wu, I-Chen
;
Hsueh, Chu-Hsuan
;
資訊工程學系
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Department of Computer Science
In this paper, we solve the portfolio selection problem. In our approach, we first propose a modified immune algorithm (IA) to reuse the memory cells we got in earlier stages, so that more information can be utilized in the next stages. Our experimental results show that the modified IA can successfully obtain significantly higher return than genetic algorithm (GA) and particle swarm optimization (PSO). Second, we. also propose a hybrid of IA and PSO (IA-PSO), and a hybrid of GA and PSO. From our experiments, the hybrid IA-PSO maintains the high return while becoming more stable.
Electrical Impedance Tomography: A Reconstruction Method Based on Neural Networks and Particle Swarm Optimization
(2015-01-01)
Martin, Sebastien
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Choi, Charles T. M.
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交大名義發表
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National Chiao Tung University
Electrical Impedance Tomography (EIT) is a non-invasive image reconstruction technique. Typically, an EIT scheme involves the solution to an inverse problem, which usually gives a poor resolution, due to linearization and ill-posedness of the problem. An alternative approach based on Artificial Neural Networks (ANN) has been used as a replacement of the inverse problem, giving correct results without linearizing the problem. However, training an ANN may be time consuming and usually requires a large amount of iterations before achieving a correct answer to the input stimulation. Several studies focused on training ANNs, and Evolutionary Algorithms (EA) gives a faster global convergence. In this paper, a novel approach based on Artificial Neural Networks and Particle Swarm Optimization (PSO) is proposed to improve the training process. A training method based on PSO algorithm achieves a faster global convergence.
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
;
Lin, Chin-Teng
;
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.
Discrete particle swarm optimization for materials acquisition in multi-unit libraries
(2012)
Wu, Yi-Ling
;
Shyu, Shyong Jian
;
Ho, Tsu-Feng
;
Lin, Bertrand M. T.
;
資訊管理與財務金融系註：原資管所+財金所
With limited budget and inflation rate, the materials acquisition in multi-unit libraries has been a challenging issue all over the world. The materials acquisition for multi-unit libraries can be regarded as a generalized version of the knapsack problem, which was known to be NP-hard, with much more constraints. Thus, it can be computationally expensive to solve the problem. In this paper, the materials acquisition problem in multi-unit libraries is formulated as an integer programming model, and two different constraint-handling mechanisms applied in discrete particle swarm optimization algorithm for obtaining the near optimal solution are presented. It is evident from our computational results that one constraint-handling mechanism can solve the problem effectively and efficiently, while the other one takes more time.
Introducing recombination with dynamic linkage discovery to particle swarm optimization
(2006)
Jian, Ming-chung
;
Chen, Ying-ping
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資訊工程學系
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Department of Computer Science
In this paper, we introduce the recombination operator with the technique of dynamic linkage discovery to particle swarm optimization (PSO) in order to improve the performance of PSO. Numerical experiments are conducted on a set of carefully designed benchmark functions and demonstrate good performance achieved by the proposed methodology.
A Hybridization of Immune Algorithm with Particle Swarm Optimization for Neuro-Fuzzy Classifiers
(2008-09-01)
Lin, Chin-Teng
;
Yang, Chien-Ting
;
Su, Miin-Tsair
;
電控工程研究所
;
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.

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