• Title, Summary, Keyword: inference system

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Learning and inference of fuzzy inference system with fuzzy neural network (퍼지 신경망을 이용한 퍼지 추론 시스템의 학습 및 추론)

  • 장대식;최형일
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.2
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    • pp.118-130
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    • 1996
  • Fuzzy inference is very useful in expressing ambiguous problems quantitatively and solving them. But like the most of the knowledge based inference systems. It has many difficulties in constructing rules and no learning capability is available. In this paper, we proposed a fuzzy inference system based on fuzy associative memory to solve such problems. The inference system proposed in this paper is mainly composed of learning phase and inference phase. In the learning phase, the system initializes it's basic structure by determining fuzzy membership functions, and constructs fuzzy rules in the form of weights using learning function of fuzzy associative memory. In the inference phase, the system conducts actual inference using the constructed fuzzy rules. We applied the fuzzy inference system proposed in this paper to a pattern classification problem and show the results in the experiment.

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Object Recognition Using Neuro-Fuzzy Inference System (뉴로-퍼지 추론 시스템을 이용한 물체인식)

  • 김형근;최갑석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.17 no.5
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    • pp.482-494
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    • 1992
  • In this paper, the neuro-fuzzy inferene system for the effective object recognition is studied. The proposed neuro-fuzzy inference system combines learning capability of neural network with inference process of fuzzy theory, and the system executes the fuzzy inference by neural network automatically. The proposed system consists of the antecedence neural network, the consequent neural network, and the fuzzy operational part, For dissolving the ambiguity of recognition due to input variance in the neuro-fuzzy inference system, the antecedence’s fuzzy proposition of the inference rules are automatically produced by error back propagation learining rule. Therefore, when the fuzzy inference is made, the shape of membership functions os adaptively modified according to the variation. The antecedence neural netwerk constructs a separated MNN(Model Classification Neural Network)and LNN(Line segment Classification Neural Networks)for dissolving the degradation of recognition rate. The antecedence neural network can overcome the limitation of boundary decisoion characteristics of nrural network due to the similarity of extracted features. The increased recognition rate is gained by the consequent neural network which is designed to learn inference rules for the effective system output.

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An Expert System for the Real-Time Computer Control of the Large-Scale System (대규모 시스템의 실시간 컴퓨터 제어를 위한 전문가 시스템)

  • Ko, Yun-Seok
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.6
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    • pp.781-788
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    • 1999
  • In this paper, an expert system is proposed, which can be effectively applied to the large-scale systems with the diversity time constraints, the objectives and the unfixed system structure. The inference scheme of the expert system have the integrated structure composed of the intuitive inference module and logical inference module in order to support effectively the operating constraints of system. The intuitive inference module is designed using the pattern matching or pattern recognition method in order to search a same or similar pattern under the fixed system structure. On the other hand, the logical inference module is designed as the structure with the multiple inference mode based on the heuristic search method in order to determine the optimal or near optimal control strategies satisfing the time constraints for system events under the unfixed system structure, and in order to use as knowledge generator. Here, inference mode consists of the best-first, the local-minimum tree, the breadth-iterative, the limited search width/time method. Finally, the application results for large-scale distribution SCADA system proves that the inference scheme of the expert system is very effective for the large-scale system. The expert system is implemented in C language for the dynamic mamory allocation method, database interface, compatability.

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Development of a Rule-Based Inference Model for Human Sensibility Engineering System

  • Yang Sun-Mo;Ahn Beumjun;Seo Kwang-Kyu
    • Journal of Mechanical Science and Technology
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    • v.19 no.3
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    • pp.743-755
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    • 2005
  • Human Sensibility Engineering System (HSES) has been applied to product development for customer's satisfaction based on ergonomic technology. The system is composed of three parts such as human sensibility analysis, inference mechanism, and presentation technologies. Inference mechanism translating human sensibility into design elements plays an important role in the HSES. In this paper, we propose a rule-based inference model for HSES. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected human sensibility words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model.

A Study on the Pattern Recognition Using Support Vector Fuzzy Inference System (Support Vector Fuzzy Inference System을 이용한 Pattern Recognition 에 관한 연구)

  • 김용균;정은화
    • Proceedings of the Korea Multimedia Society Conference
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    • pp.374-379
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    • 2003
  • 본 논문에서는 pattern recognition을 위하여 support vector fuzzy inference system을 제안하였다 Fuzzy inference system의 structure와 parameter를 identification 하기 위하여 Support vector machine을 이용하였으며 에러 최소화 기법으로는 gradient descent 방법을 사용하였다. 제안된 SVFIS 방법의 성능을 파악하고자 COIL 이미지를 이용한 3차원 물체 인식 실험을 수행하였다.

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RDB-based Automatic Knowledge Acquisition and Forward Inference Mechanism for Self-Evolving Expert Systems

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.6
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    • pp.743-748
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    • 2003
  • In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database (RDB) and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert system. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently. and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

Applying A Matrix-Based Inference Algorithm to Electronic Commerce

  • Lee, kun-Chang;Cho, Hyung-Rae
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • pp.353-359
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    • 1999
  • We present a matrix-based inference alorithm suitable for electronic commerce applications. For this purpose, an Extended AND-OR Graph (EAOG) was developed with the intention that fast inference process is enabled within the electronic commerce situations. The proposed EAOG inference mechanism has the following three characteristics. 1. Real-time inference: The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matric computation.2. Matrix operation: All the subjective knowledge is delineated in a matrix form, so that inference process can proceed based on the matrix operation which is computationally efficient.3. Bi-directional inference: Traditional inference method of expert systems is based on either forward chaining or backward chaining which is mutually exclusive in terms of logical process and exclusive in terms of logical process and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency. We have proved the validity of our approach with several propositions and an illustrative EC example.

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Parallel Fuzzy Inference Method for Large Volumes of Satellite Images

  • Lee, Sang-Gu
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.1 no.1
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    • pp.119-124
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    • 2001
  • In this pattern recognition on the large volumes of remote sensing satellite images, the inference time is much increased. In the case of the remote sensing data [5] having 4 wavebands, the 778 training patterns are learned. Each land cover pattern is classified by using 159, 900 patterns including the trained patterns. For the fuzzy classification, the 778 fuzzy rules are generated. Each fuzzy rule has 4 fuzzy variables in the condition part. Therefore, high performance parallel fuzzy inference system is needed. In this paper, we propose a novel parallel fuzzy inference system on T3E parallel computer. In this, fuzzy rules are distributed and executed simultaneously. The ONE_To_ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This system can be used in a large expert system that ha many inference variables in the condition and the consequent part.

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Fault Diagnosis in Gas Turbine Engine Using Fuzzy Inference Logic (퍼지 로직 시스템을 이용한 항공기 가스터빈 엔진 오류 검출에 대한 연구)

  • Mo, Eun-Jong;Jie, Min-Seok;Kim, Chin-Su;Lee, Kang-Woong
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.1
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    • pp.49-53
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    • 2008
  • A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

A Development of Forward Inference Engine and Expert Systems based on Relational Database and SQL

  • Kim, Jin-Sung
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.49-52
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    • 2003
  • In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert systems. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently, and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

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