• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제3권3호
• /
• pp.309-330
• /
• 2009

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

• 정보학연구
• /
• 제8권1호
• /
• pp.51-56
• /
• 2005

The Back propagation algorithm is a very effective supervised training method for multi-layer feed forward neural networks. This paper studies the decision boundary formation based on the Back propagation algorithm. The discriminating powers of several neural network topology are also investigated against five manually created data sets. It is found that neural networks with multiple hidden layer perform better than single hidden layer.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.462-462
• /
• 2000

In this paper, genetic algorithm (GA) is the technique to search for the optimal structures (i,e., the kind of neural network, the number of hidden neuron, ..) of the neural networks which are used approximating a given nonlinear function, In this paper, we used multi layer feed-forward neural network. The decision method of synapse weights of each neuron in each generation used back-propagation method. In this study, we simulated nonlinear function approximation in the temperature control system.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
• /
• pp.49-52
• /
• 1998

Neural Network has good performance in pattern classification, control and many other fields by learning ability. However, there is effective rule or systematic approach to determine optimal structure. In this paper, we propose a new method to find optimal structure of feed-forward multi-layer neural network as a kind of pruning method. That eliminating redundant elements of neural network. To find redundant elements we analysis error and weight changing with Rough Set Theory, in condition of executing back-propagation leaning algorithm.

• Structural Engineering and Mechanics
• /
• 제26권4호
• /
• pp.377-392
• /
• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• Journal of Advanced Marine Engineering and Technology
• /
• 제23권4호
• /
• pp.480-487
• /
• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• 대한전기학회논문지
• /
• 제45권2호
• /
• pp.325-328
• /
• 1996

In this paper, we proposed an estimation method of a posterior probability and PDF(Probability density function) using a feed forward neural network and code books of VQ(vector quantization). In this study, We estimates a posterior probability and probability density function, which compose a new parameter with well-known Mel cepstrum and verificate the performance for the five vowels taking from syllables by NN(neural network) and PNN(probabilistic neural network). In case of new parameter, showed the best result by probabilistic neural network and recognition rates are average 83.02%.

• Journal of Communications and Networks
• /
• 제5권1호
• /
• pp.7-18
• /
• 2003

In this paper, a decision feedback recurrent neural network equalizer and a modified real time recurrent learning algorithm are proposed, and an adaptive adjusting of the learning step is also brought forward. Then, a complex case is considered. A decision feedback complex recurrent neural network equalizer and a modified complex real time recurrent learning algorithm are proposed. Moreover, weights of decision feedback recurrent neural network equalizer under burst-interference conditions are analyzed, and two anti-burst-interference algorithms to prevent equalizer from out of working are presented, which are applied to both real and complex cases. The performance of the recurrent neural network equalizer is analyzed based on numerical results.

• 한국정보통신학회논문지
• /
• 제5권6호
• /
• pp.1122-1127
• /
• 2001

본 논문에서는 공기압축기 구동용 유도전동기를 대상으로 부하토크관측기와 신경망을 이용 한 피드포워드 보상기를 결합한 속도 적응제어시스템을 제안한다. 공기압축기를 구동하는 전동기는 피스톤 의 상하운동에 의해 급격한 가변형의 부하를 받게 되고, 이로 인해 운전특성에 문제가 발생된다. 신경망 추정기를 이용하여 속도 제어기의 이득을 실시간으로 동조함으로써 전동기의 속도제어 특성을 개선한다. 제안된 시스템에 대한 이론적 해석과 시뮬레이션을 통해 그 타당성을 검정한다.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2001년도 추계종합학술대회
• /
• pp.397-400
• /
• 2001

본 논문에서는 공기압축기 구동용 유도전동기를 대상으로 부하토크관측기와 신경망을 이용한 피드포워드 보상기를 결합한 속도 적응제어시스템을 제안한다. 공기압축기를 구동하는 전동기는 피스톤의 상하운동에 의해 급격한 가변형의 부하를 받게 되고, 이로 인해 운전특성에 문제가 발생된다. 신경망 추정기를 이용하여 속도 제어기의 이득을 실시간으로 동조함으로써 전동기의 속도제어 특성을 개선한다. 제안된 시스템에 대한 이론적 해석과 시뮬레이션을 통해 그 타당성을 검정한다.