• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.680-688
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• 2001

In this paper, we propose a reliable method for searching the optimally performing generalized brain-state-in-a-box (GBSB) neural associative memory using an evolution program (EP) given a set of prototype patterns to be stored as stable equilibrium points. First, we exploit some qualitative guidelines necessary to synthesize the GBSB model. Next, we parameterize the solution space utilizing the limited number of parameters to represent the solution space. Then, we recast the synthesis of GBSB neural associative memories as two constrained optimization problems, which are equivalent to finding a solution to the original synthesis problem. Finally, we employ an evolution program (EP), which enables us to find an optimal set of parameters related to the size of domains of attraction (DOA) for prototype patterns. The validity of this approach is illustrated by a design example and computer simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5489-5511
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• 2019

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.220-222
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• 2006

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated lineal operators to be matched. Also, by using the bilinear and inverse bilinear approximation method, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). Finally, a T-S fuzzy model for the chaotic Lorentz system is used as an example to guarantee the stability and effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.25-33
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• 1997

A DEDS is a system whose states change in response to the occurence of events from a predefined event set. In this paper, we consider the optimal control and reasoning problem for Discrete Event Systems(DES) in the Temporal Logic Framework(TEL) which have been recnetly defined. The TLE is enhanced with objective functions(event cost indices) and a measurement space is alos deined. A sequence of event which drive the system form a give initial state to a given final state is generated by minimizing a cost functioin index. Our research goal is the reasoning of optimal trajectory and the design of the optimal controller for DESs. This procedure could be guided by the heuristic search methods. For the heuristic search, we suggested the Stochastic Ruler algorithm, instead of the A algorithm with difficulties as following ; the uniqueness of solutions, the computational complexity and how to select a heuristic function. This SR algorithm is used for solving the optimal problem. An example is shown to illustrate our results.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.496-498
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• 2005

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs).

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.15-23
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• 2008

This paper considers the problem of delay-dependent guaranteed cost controller design for uncertain neutral systems with distributed delays. The system under consideration is subject to norm-bounded time-varying parametric uncertainty appearing in all the matrices of the state-space model. By constructing appropriate Lyapunov functionals and using matrix inequality techniques, a state feedback controller is designed such that the resulting closed-loop system is not only robustly stable but also guarantees an adequate level of performance for all admissible uncertainties. Furthermore, a convex optimization problem is introduced to minimize a specified cost bound. By matrix transformation techniques, the corresponding optimal guaranteed controller can be obtained by solving a linear matrix inequality. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed approach.

• Advances in Energy Research
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• v.8 no.3
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• pp.145-153
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• 2022

Recent concerns about rising fuel prices and greenhouse gas emissions have focused attention on alternative energy sources, particularly in the transport sector. Transportation consumes 40% of overall fuel usage. As a result, a growing majority of researches on Electric Vehicles (EVs) and their Energy Management Systems (EMS) have been done. In order to enhance the performance and to meet the needs of drivers, more information regarding the EMS is needed. A new Energy Management System is proposed using a FOPID controller. To put the concept into practice, state equations are utilised. The fifth-order state-space model under study is a linked model with several inputs and outputs and the transfer matrices are calculated for decoupling the system. Utilizing these transfer matrices to decouple the system and FOPID controller is used to tune the system. The tuned parameters are minimized using a Particle Swarm Optimization (PSO) approach with Integral Time Absolute Error (ITAE) as the goal. When the suggested FOPID system's results are compared to those of PID-controlled systems, a sizable improvement is observed, which is explained by the results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.117-126
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• 1992

This study proposes a new interactive multicriteria method for determining the best levels of the decision variables needed to optimize a stochastic computer simulation with multiple response variables. The method, called the Pairwise Comparison Stochastic Cutting Plane (PCSCP) method, combines good features from interactive multiple objective mathematical programming methods and response surface methodology. The major characteristics of the PCSCP algorithm are: (1) it interacts progressively with the decision maker (DM) to obtain his preferences, (2) it uses good experimental design to adequately explore the decision space while reducing the burden on the DM, and (3) it uses the preference information provided by the DM and the sampling error in the responses to reduce the decision space. This paper presents the basic concepts of the PCSCP method along with its performance for solving randomly selected test problems.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.2
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• pp.121-128
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• 2011

As high precision industries such as semiconductor, TFT-LCD manufacturing and MEMS continue to grow, the demand for higher DOF precision stages has been increasing. In general, the stages should accommodate a prescribed range of payloads in order to position various precision manufacturing/inspection instruments. Therefore a nonlinear controller using sliding motion is developed, which bears mass perturbation and makes the upper plate of the stage move in 6 DOF. For the application of the nonlinear control, an observer is also developed based on expected noise covariance. To eliminate the steady state error of step response, integral terms are inserted into the state-space model. The linear term of the controller is designed using optimization scheme in which parameters can be weighted according to their physical significance, whereas the nonlinear term of the controller is designed using trial and error method. A comprehensive simulation study proves that the designed controller is robust against mass perturbation and completely eliminates steady state errors.

• Journal of Korean Association for Spatial Structures
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• v.1 no.1 s.1
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• pp.125-134
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• 2001

The objective of this study is the development of size discrete optimum design algorithm which is based on the GAs(genetic algorithms). The algorithm can perform size discrete optimum designs of space trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses and the constraints are limite state design codes(1998) and displacements. The basic search method for the optimum design is the GAs. The algorithm is known to be very efficient for the discrete optimization. This study solves the problem by introducing the GAs. The GAs consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. In the genetic process of the simple GAs, there are three basic operators: reproduction, cross-over, and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying GAs to optimum design examples.