• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.05a
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.2
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• pp.383-394
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• 2001

The stabilization control of inverted pendulum (IP) system is difficult because of its nonlinearity and structural unstability. In this paper, an Evolving Neural Network Controller (ENNC) without Error Back Propagation (EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC)are compared with the one of conventional optimal controller and the conventional evolving neural network controller (CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.157-163
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• 2000

In this paper, an Evolving Neural Network Controller(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algoithm(RVEGA) was presented for stabilization of an Inverter Pendulum(IP) system with nonlinearity. This proposed ENNC was described by a simple genetic chromosome. And the deletion of neuron, the determinations of input or output neuron, the deleted neuron and the activation functions types are given according to the various flag types. Therefore, the connection weights, its structure and the neuron types in the given ENNC can be optimized by the proposed evolution strategy. Through the simulations, we showed that the finally acquired optimal ENNC was successfully applied to the stabilization control of an IP system.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.963-965
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• 1999

A linear chromosome combined with a grid-based representation of the network and a new crossover operator allow the evolution of the architecture and the weights simultaneously. In our approach there is no need for a separate weight optimization procedure and networks with more than one type of activation function can be evolved. In this paper one evolutionary' strategy of a given dual neural controller was introduced and the simulation results were described in detail through applications to a stabilization control of an Inverted Pendulum System.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3013-3015
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• 2000

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of conventional techniques such as the pole placement and the optimal control based on the local linearizations have narrow stabilizable regions, At the same time, the fine tunings of their gain parameters are also troublesome, Thus, in this paper, an Evolving Neural Network ControlleY(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algorithm (RVEGA) was presented for stabilization of an IP system with nonlinearity, This proposed ENNC was described by a simple genetic chromosome. Through the simulation and experimental results, we showed that the finally acquired optimal ENNC was very useful in the stabilization control of IP system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.3
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• pp.68-76
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• 2000

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of conventional techniques such as the pole placement and the optimal control based on the local linearizations have narrow stabilizable regions. At the same time, the fine tunings of their gain parameters are also troublesome. Thus, in this paper, an Evolving Neural Network Controller(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algorithm(RVEGA) was presented for stabilization of an IP system with nonlinearity. This proposed ENNC was described by a simple genetic chromosome. And the deletion of neuron, the according to the various flag types. Therefore, the connection weights, its structure and the neuron types in the given ENNC can be optimized by the proposed evolution strategy. And the proposed ENNC was implemented successfully on the ADA-2310 data acquisition board and the 80586 microprocessor in order to stabilize the IP system. Through the simulation and experimental results, we showed that the finally acquired optimal ENNC was very useful in the stabilization control of IP system.