• Proceedings of the KIEE Conference
• /
• 1994.07a
• /
• pp.331-333
• /
• 1994

A learning method for induction motor trajectory using neuro-fuzzy networks (NFN) based on fusion of fuzzy logic theory and neural networks is proposed. The premise and consequent parameters of the NFN affecting the controllers performances are modified during the learning stages by the proposed learning method to implement an optimal controller only with pre-determined target trajectory and the least amount of knowledge about an induction motor. The induction motor position control system is simulated to verify the effectiveness of the learned NF controller(NFC). The simulation results shows that the proposed learning method has good dynamic performance and small steady state error.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.11
• /
• pp.2189-2198
• /
• 1994

In this paper, a self-tuning fuzzy logig controller is implemented to control a DC servo motor by the self-tuning technique based on fuzzy meta-rules with learning in several algorithms to improve the performance of the fuzzy logic controller used in a fuzzy control system. Simulations and experimental results of the self-tuning fuzzy logic controller are compared with those of the fuzzy logic controller to evaluate its performance.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.131-134
• /
• 1997

This paper presents a new approach to the design of intelligent control system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.6
• /
• pp.1872-1879
• /
• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

• Journal of the Korean Society of Industry Convergence
• /
• v.6 no.1
• /
• pp.37-42
• /
• 2003

In this paper a self-organizing fuzzy system with rule pruning is proposed. A conventional self-organizing fuzzy system having only rule generation has a drawback in generating many slightly different rules from the existing rules which results in increased computation time and slowly learning. The proposed self-organizing fuzzy system generates fuzzy rules based on input-output data and prunes redundant rules which are caused by parameter training. The proposed system has a simple structure but performs almost equivalent function to the conventional self-organizing fuzzy system. Also, this system has better learning speed than the conventional system. Simulation results on several numerical examples demonstrate the performance of the proposed system.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.133-139
• /
• 1997

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.45-49
• /
• 1997

In this paper, we controlled a Rotary Inverted Pendulum System using Neuro-Fuzzy Controller(NFC). The inverted pendulum system is widely used as a typical example of an unstable nonlinear control system which is difficult to control. Fuzzy theory have been because membership functions and rules of a fuzzy controller are often given by experts or a fuzzy logic control system. This controller is a feedforward multilayered network which integrates the basic elements and functions of a tradtional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such NFC can be constructed from training examples by learning rule, and the structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Using this controller, we presented the results that controlled a Rotary Inverted Pendulum System and the associated algorithms.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.5
• /
• pp.395-399
• /
• 2001

This paper describes modular fuzzy inference systems(MFIS) with adaptive capability to extract fuzzy inference modules from observation data through the learning process. The proposed MFIS is based on the structural similarity to Tagaki-Sugeno fuzzy models and a modular neural architecture. The learning of MFIS is done by assigning new fuzzy inference modules and by updating the parameters of existing modules. The fuzzy inference modules consist of local model network and fuzzy gating network. The parameters of the MFIS are updated by the standard LMS algorithm. The performance of the MFIS is illustrated with adaptive control of a nonlinear dynamic system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.10a
• /
• pp.142-147
• /
• 1999

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.4
• /
• pp.832-839
• /
• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.