• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2414-2416
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• 2003

In this raper, we present a Wavelet Neural Network(WNN) approach to the solution of the tracking problem for mobile robots that possess complexity, nonlinearity and uncertainty. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome the problems caused by local minima of optimization and various uncertainties. This network structure is helpful to determine the number of the hidden nodes and the initial value of weights with compact structure. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by the gradient-descent method. Through computer simulations, we demonstrate the effectiveness and feasibility of the proposed control method.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.108-117
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• 1996

In this paper, we propose a new type of recurrent neural network architecture in which each output unit is connected with itself and fully-connected with other output units and all hidden units. The proposed recurrent network differs from Jordan's and Elman's recurrent networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving the discrimination and generalization power. We also prove the convergence property of learning algorithm of the proposed recurrent neural network and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. Experimental results confirmed that the proposed recurrent neural network improves the discrimination and generalization power in recognizing spatial patterns.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2309-2314
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• 1997

The study introduces a neural network to predict the bankruptcy of insurance companies. As a method to optimize the network, a genetic algorithm suggests optimal structure and network parameters. The neural network designed by genetic algorithm is compared with discriminant analysis, logistic regression, ID3, and CART. The robust neural network model shows the best performance among those models compared.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.324-334
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• 2016

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.103-106
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• 2003

An oscillatory neural network circuit has been designed and fabricated in an 0.5 ${\mu}{\textrm}{m}$ double poly CMOS technology. The proposed oscillatory neural network consists of 3 neural oscillator cells with excitatory synapses and a neural oscillator cell with inhibitory synapse. Simulations of a network of oscillators demonstrate cooperative computation. Measurements of the fabricated chip in condition of $\pm$ 2.5 V power supply is shown.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.243-247
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• 1992

This paper describes a neural network based control scheme with MRAC. The system consists of two neural network; one is for identifier and the other is for controller. Identification is firstly performed to learn the behavior of the nonlinear plant. Neural net controller is next trained by backpropagating the error at the output of plant through the identifier. Also the training method used in this paper repeatedly updates weights of neural network to track the reference model.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.74-74
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• 2000

This paper presents a neural network controller for a rigid-link electrically-driven robot. The proposed controller is designed in conjunction with three neural networks approximating for complicated nonlinear functions. Particularly, the fact, different from conventional schemes, is that the neural network based current observer is used. Therefore, no accurate measurement of the actuator driving current is required. In the proposed controller-observer scheme, the derived weight update rule guarantees the stability of closed-loop system in the sense of Lyapunov. The effectiveness and performance of the proposed method are demonstrated through computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.5
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• pp.519-526
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• 1990

In this paper we propose the resolved motion controller using a neural network for a robot manipulator. Neural identifier designed by a neural network is trained by using a feedback force as an error signal. The identifier approximates the output of a unknown nonlinear system by monitoring both the input and the output of this system. If the neural network is sufficiently trained well, it does not require either strict modelling of the manipulator or precise parameter estimation. The effectiveness of the proposed controller is demonstrated by computer simulation using a two-link planar robot.