• Title/Summary/Keyword: neural logic

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A Study on the Digital Implementation of Multi-layered Neural Networks for Pattern Recognition (패턴인식을 위한 다층 신경망의 디지털 구현에 관한 연구)

  • 박영석
    • Journal of the Institute of Convergence Signal Processing
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    • v.2 no.2
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    • pp.111-118
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    • 2001
  • In this paper, in order to implement the multi-layered perceptron neural network using pure digital logic circuit model, we propose the new logic neuron structure, the digital canonical multi-layered logic neural network structure, and the multi-stage multi-layered logic neural network structure for pattern recognition applications. And we show that the proposed approach provides an incremental additive learning algorithm, which is very simple and effective.

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A Construction of Fuzzy Inference Network based on Neural Logic Network and its Search Strategy

  • Lee, Mal-rey
    • Proceedings of the Korea Society for Industrial Systems Conference
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    • 2000.11a
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    • pp.375-389
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    • 2000
  • Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule- inference. network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search costs for searching sequentially and searching by means of search priorities.

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Test Generation for Combinational Logic Circuits Using Neural Networks (신경회로망을 이용한 조합 논리회로의 테스트 생성)

  • 김영우;임인칠
    • Journal of the Korean Institute of Telematics and Electronics A
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    • v.30A no.9
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    • pp.71-79
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    • 1993
  • This paper proposes a new test pattern generation methodology for combinational logic circuits using neural networks based on a modular structure. The CUT (Circuit Under Test) is described in our gate level hardware description language. By conferring neural database, the CUT is compiled to an ATPG (Automatic Test Pattern Generation) neural network. Each logic gate in CUT is represented as a discrete Hopfield network. Such a neual network is called a gate module in this paper. All the gate modules for a CUT form an ATPG neural network by connecting each module through message passing paths by which the states of modules are transferred to their adjacent modules. A fault is injected by setting the activation values of some neurons at given values and by invalidating connections between some gate modules. A test pattern for an injected fault is obtained when all gate modules in the ATPG neural network are stabilized through evolution and mutual interactions. The proposed methodology is efficient for test generation, known to be NP-complete, through its massive paralelism. Some results on combinational logic circuits confirm the feasibility of the proposed methodology.

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A New Basic Element for Neural Logic Functions and Capability in Circuit Applications

  • Omura, Yasuhisa
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.2 no.1
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    • pp.70-81
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    • 2002
  • This paper describes a new basic element which shows a synaptic operation for neural logic applications and shows function feasibility. A key device for the logic operation is the insulated-gate pn-junction device on SOI substrates. The basic element allows an interface quite compatible to that of conventional CMOS circuits and vMOS circuits.

ON THE STRUCTURE AND LEARNING OF NEURAL-NETWORK-BASED FUZZY LOGIC CONTROL SYSTEMS

  • C.T. Lin;Lee, C.S. George
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.993-996
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    • 1993
  • This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

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Development of Integrated Control Methods for the Heating Device and Surface Openings based on the Performance Tests of the Rule-Based and Artificial-Neural-Network-Based Control Logics (난방시스템 및 개구부의 통합제어를 위한 규칙기반제어법 및 인공신경망기반제어법의 성능비교)

  • Moon, Jin Woo
    • KIEAE Journal
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    • v.14 no.3
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    • pp.97-103
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    • 2014
  • This study aimed at developing integrated logic for controlling heating device and openings of the double skin facade buildings. Two major logics were developed-rule-based control logic and artificial neural network based control logic. The rule based logic represented the widely applied conventional method while the artificial neural network based logic meant the optimal method. Applying the optimal method, the predictive and adaptive controls were feasible for supplying the advanced thermal indoor environment. Comparative performance tests were conducted using the numerical computer simulation tools such as MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation). Analysis on the test results in the test module revealed that the artificial neural network-based control logics provided more comfortable and stable temperature conditions based on the optimal control of the heating device and opening conditions of the double skin facades. However, the amount of heat supply to the indoor space by the optimal method was increased for the better thermal conditioning. The number of on/off moments of the heating device, on the other hand, was significantly reduced. Therefore, the optimal logic is expected to beneficial to create more comfortable thermal environment and to potentially prevent system degradation.

Image Recognition by Learning Multi-Valued Logic Neural Network

  • Kim, Doo-Ywan;Chung, Hwan-Mook
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.2 no.3
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    • pp.215-220
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    • 2002
  • This paper proposes a method to apply the Backpropagation(BP) algorithm of MVL(Multi-Valued Logic) Neural Network to pattern recognition. It extracts the property of an object density about an original pattern necessary for pattern processing and makes the property of the object density mapped to MVL. In addition, because it team the pattern by using multiple valued logic, it can reduce time f3r pattern and space fer memory to a minimum. There is, however, a demerit that existed MVL cannot adapt the change of circumstance. Through changing input into MVL function, not direct input of an existed Multiple pattern, and making it each variable loam by neural network after calculating each variable into liter function. Error has been reduced and convergence speed has become fast.

Neural Logic Network-Based Fuzzy Inference Network and its Search Strategy (신경논리망 기반의 퍼지추론 네트워크와 탐색 전략)

  • Lee, Heon-Joo;Kim, Jae-Ho
    • The Transactions of the Korea Information Processing Society
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    • v.3 no.5
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    • pp.1138-1146
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    • 1996
  • Fuzzy logic ignores some informations in the reasoning process. Neural networks are powerful tools for the pattern processing. However, to model human knowledges, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy logical reasoning, we construct fuzzy inference net-work based on the neural logic network, extending the existing rule-inferencing network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search cost for searching sequentially and searching by means of priorities.

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A Study on the Load Frequency Control of 2-Area Power System using Fuzzy-Neural Network Controller (퍼지-신경망 제어기를 이용한 2지역 계통의 부하주파수제어에 관한연구)

  • Chung, Hyeng-Hwan;Kim, Sang-Hyo;Joo, Seok-Min;Lee, Jeong-Phil;Lee, Dong-Chul
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.2
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    • pp.97-106
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    • 1999
  • This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.

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Fuzzy Inference Network and Search Strategy using Neural Logic Network (신경논리망을 이용한 퍼지추론 네트워크와 탐색전략)

  • 이말례
    • Journal of Korea Multimedia Society
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    • v.4 no.2
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    • pp.189-196
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    • 2001
  • Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule - inference network. and the traditional propagation rule is modified.

  • PDF