Browsing by Author "Wu, Cheng-Tao"

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Adaptive neural-based fuzzy modeling for biological systems
(2013-04-01)
Wu, Shinq-Jen
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Wu, Cheng-Tao
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Chang, Jyh-Yeong
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電控工程研究所
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Institute of Electrical and Control Engineering
The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems. (C) 2013 Elsevier Inc. All rights reserved.
Balancing control of sliding inverted-wedge system: classical-method-based compensation
(2006)
Wu, Shinq-Jen
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Wu, Cheng-Tao
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Chiou, Yung-Yi
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Lin, Chin-Teng
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Chung, Yi-Nung
Root-locus-based PID controller and LQR-based controller always fail as system nonlinearity increases. We here propose the optimization-compensated block/diagram to reinforce the stabilization ability of these two classical control methods for nonlinear system, and besides, to achieve other performance requirements such as constrained overshoot and fast response. The controller design of a nonlinear sliding weights balancing mechanism is based on optimization-compensated root locus and LQR method. First, according to root-locus of the linearized dynamic system, we propose extra poles and zeros addition to roughly draw the locus shifting to left to achieve stabilization requirement. The poles and zeros are realized by P/PD/PID controllers. For LQR approach, we choose performance parameters to meet stabilization and minimum energy requirement. The controller is realized as feedback controller. Further, to compensate the model-error from nonlinearity and to meet other performance such as overshoot and setting time, some P/PID parameters for root-locus method and the feedback gain for LQR method are optimized via optimal parameter searching in NCD/Matlab toolbox. The simulation results demonstrate the stability and the constrained performances of the entire closed-loop system can be ensured by the proposed compensated control block diagrams.
Computation intelligent foir eukaryotic cell-cycle gene network
(2006)
Wu, Shinq-Jen
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Wu, Cheng-Tao
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Lee, Tsu-Tian
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電控工程研究所
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Institute of Electrical and Control Engineering
Computational intelligent approaches is adopted to construct the S-system of eukaryotic cell cycle for further analysis of genetic regulatory networks. A highly nonlinear power-law differential equation is constructed to describe the transcriptional regulation of gene network from the time-courses dataset. Global artificial algorithm, based on hybrid differential evolution, can achieve global optimization for the highly nonlinear differential gene network modeling. The constructed gene regulatory networks will be a reference for researchers to realize the inhibitory and activatory operator for genes synthesis and decomposition in Eukaryotic cell cycle.
Computational optimization for S-type biological systems: Cockroach genetic algorithm
(2013-10-01)
Wu, Shinq-Jen
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Wu, Cheng-Tao
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電控工程研究所
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Institute of Electrical and Control Engineering
S-type biological systems (S-systems) are demonstrated to be universal approximations of continuous biological systems. S-systems are easy to be generalized to large systems. The systems are identified through data-driven identification techniques (cluster-based algorithms or computational methods). However, S-systems' identification is challenging because multiple attractors exist in such highly nonlinear systems. Moreover, in some biological systems the interactive effect cannot be neglected even the interaction order is small. Therefore, learning should be focused on increasing the gap between the true and redundant interaction. In addition, a wide searching space is necessary because no prior information is provided. The used technologies should have the ability to achieve convergence enhancement and diversity preservation. Cockroaches live in nearly all habitats and survive for more than 300 million years. In this paper, we mimic cockroaches' competitive swarm behavior and integrated it with advanced evolutionary operations. The proposed cockroach genetic algorithm (CGA) possesses strong snatching-food ability to rush forward to a target and high migration ability to escape from local minimum. CGA was tested with three small-scale systems, a twenty-state medium-scale system and a thirty-state large-scale system. A wide search space ([0,100] for rate constants and [-100,100] for kinetic orders) with random or bad initial starts are used to show the high exploration performance. (C) 2013 Elsevier Inc. All rights reserved.
Fuzzy-Based Self-Interactive Multiobjective Evolution Optimization for Reverse Engineering of Biological Networks
(2012-10-01)
Wu, Shinq-Jen
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Wu, Cheng-Tao
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Chang, Jyh-Yeong
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交大名義發表
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電控工程研究所
S-system modeling from time series datasets can provide us with an interactive network. However, system identification is difficult since an S-system is described as highly nonlinear differential equations. Much research adopts various evolution computation technologies to identify system parameters, and some further achieve skeletal-network structure identification. However, the truncated redundant kinetic orders are not small enough as compared with the preserved terms. In this paper, we integrate quantitative genetics, bacterium movement, and fuzzy set theory into evolution computation to develop a new genetic algorithm to achieve convergence enhancement and diversity preservation. The proposed exploration and exploitation genetic algorithm (EEGA) can improve the best-so-far individual and ensure global optimal search at the same time. The EEGA enhances evolution convergence by golden section seed selection, normal-distribution reproduction, mixed inbreeding and backcrossing, competition elitism, and acceleration operations. Search-then-conquer evolution direction operations, eugenics-based screen-sifting mutation, eugenic self-mutation, and fuzzy-based tumble migration preserve population diversity to avoid premature convergence. Furthermore, to ensure that a reasonable gene regulation network is inferred, fuzzy composition is introduced to derive a reconstruction index. This performance index let EEGA possess self-interactive multiobjective learning. The proposed fuzzy-reconstruction-based multiobjective genetic algorithm is examined by three dry-lab biological systems. Simulation results show that a safety pruning action is guaranteed (the truncation threshold is set to be 10(-15)), and only one- or two-step pruning action is taken.
Inference of genetic network of Xenopus frog egg: Improved genetic algorithm
(2006)
Wu, Shinq-Jen
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Chou, Chia-Hsien
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Wu, Cheng-Tao
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Lee, Tsu-Tian
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電控工程研究所
An improved genetic algorithm (IGA) is proposed to acheive S-system gene network modeling of Xenopus frog egg. Via the time-courses training datasets from Michaelis-Menten model, the optimal parameters are learned. The S-system can clearly describe activative and inhibitory interacation between genes as generating and consuming process. We concern the mitotic control in cell-cycle of Xenopus frog egg to realize cyclin-Cdc2 and Cdc25 for MPF activity. The proposed IGA can acheive global search with migration and keep the best chromosome with elitism operation. The generated gene regulatory networks can provide biological researchers for further experiments in Xenopus frog egg cell cycle control.
Migration Synchronous Genetic Algorithm for Reverse Engineering
(2011)
Wu, Shinq-Jen
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Wu, Cheng-Tao
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電控工程研究所
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Institute of Electrical and Control Engineering
Much research adopts various evolution computation technologies to identify system parameters and structures of highly nonlinear S-system model. They, always focus on evolution skills and neglect that the choice of performance index is the key for learning. A suitable performance index not only provides good searching direction but also reduces computation time. In this study, a migration synchronous genetic algorithm (MSGA) is proposed for achieving global optimal search. Twenty eight performances of concentration- or slope-error-based indexes for parameter identification is examined and discussed. When the chosen performance candidates are used for structure identification, only one- or two-steps pruning-operation is necessary. The pruning threshold is set to be 10(-15) to ensure a safely pruning action is guaranteed positively.
Neural-fuzzy gap control for a current/voltage-controlled 1/4-vehicle MagLev system
(2008-03-01)
Wu, Shinq-Jen
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Wu, Cheng-Tao
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Chang, Yen-Chen
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電控工程研究所
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Institute of Electrical and Control Engineering
A magnetically levitated (MagLev) vehicle prototype has independent levitation (attraction) and propulsion dynamics. We focus on the levitation behavior to obtain precise gap control of a 1/4 vehicle. An electromagnetic leviation system is highly nonlinear and naturally unstable, and its equilibrium region is severely restricted. It is therefore a tough task to achieve high-performance vehicle-levitated control. In this paper, a MagLev system is modeled by two self-organizing neural-fuzzy techniques to achieve linear and affine Takagi-Sugeno (T-S) fuzzy systems. The corresponding linear-type optimal fuzzy controllers are then used to regulate both physical systems (voltage- and current-controlled systems). On the other hand, an affine-type fuzzy control design scheme is proposed for the affine-type systems. Control performance and robustness to an external disturbance are shown in simulation results. Affine T-S fuzzy representation provides one more adjustable parameter in the neural-fuzzy learning process. Therefore, an affine T-S-based controller possesses better performance for a current-controlled system since it is nonlinear not only to system states but also to system inputs. This phenomenon is shown in simulation results. Technical contributions include a nonlinear affine-type optimal fuzzy control design scheme, self-organizing neural-learning-based linear and affine T-S fuzzy modeling for both MagLev systems, and the achievement of an integrated neural-fuzzy technique to stabilize current- and voltage-controlled MagLev systems under minimal energy-consumption conditions.
Winner-Take-All Memetic Differential Evolution for Genetic Interaction: Parameter Identification
(2013-01-01)
Wu, Shinq-Jen
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Wu, Cheng-Tao
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電控工程研究所
;
Institute of Electrical and Control Engineering
The emerging large-scale biological tools (e. g., microarray) challenge biologists to realize the connectivity of genes and/or proteins at the system level (global view). Having advantages in good generalization and showing the direct interaction of genes and/or proteins, the S-system becomes one of the popular models, which is able to capture the dynamic behavior of the biological system. Differential evolution (DE) and its variants have recently applied to solve various optimization problems in engineering fields. However, the exploitative and explorative abilities are insufficient. In this study, we propose a winner-take-all memetic differential evolution scheme to infer the parameters of the S-type gene regulatory networks. This method was tested with a genetic-branch pathway and a twenty-gene network. The learning was implemented in a wide search space ([0, 100] for rate constants and [-100, 100] for kinetic orders) with a bad initial start (All parameters were randomly initialized at the neighborhood of 80). Simulation results show high-accuracy solutions are obtained.