A new approach of multi-stage fuzzy logic inference

Chen, Hung-Pin; Parng, Tai-Ming

doi:10.1016/0165-0114(95)00110-7

Cited by 23 publications

(8 citation statements)

References 16 publications

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“…As opposed to single-stage fuzzy reasoning, MSFR involves three kinds of linguistic variable [19]. They are input, output, and intermediate variables where input variables (e.g. )…”

Section: B Multistage Fuzzy Rulesmentioning

confidence: 99%

“…In [19], another study of MSFR is reported. This work concentrates on simplifying the computational complexity of MSFR methods and precomputation mechanisms have been introduced, assuming that the fuzzy rules are fixed using expert's knowledge.…”

Section: Rulementioning

confidence: 99%

“…It has been observed that many of the previous MSFR systems are based on fixed fuzzy rules as in [18] and [19]. For many practical systems, however, it is very difficult, if not impossible, to acquire a complete MSFR rule set from experts.…”

Section: Rulementioning

confidence: 99%

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On multistage fuzzy neural network modeling

Chung¹,

Duan

2000

IEEE Trans. Fuzzy Syst.

109

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In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fuzzy neural network (FNN) models have been proposed to implement different types of single-stage fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problems. To address the problem, FNN modeling based on multistage fuzzy reasoning (MSFR) is pursued here and two hierarchical network models, namely incremental type and aggregated type, are introduced. The new models called multistage FNN (MSFNN) model a hierarchical fuzzy rule set that allows the consequence of a rule passed to another as a fact through the intermediate variables.From the stipulated input-output data pairs, they can generate an appropriate fuzzy rule set through structure and parameter learning procedures proposed in this paper. In addition, we have particularly addressed the input selection problem of these two types of multistage network models and proposed two efficient methods for them. The effectiveness of the proposed MSFNN models in handling high-dimensional problems is demonstrated through various numerical simulations.Index Terms-Curse of dimensionality, fuzzy neural networks, hierarchical modeling, multistage fuzzy reasoning.

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“…As opposed to single-stage fuzzy reasoning, MSFR involves three kinds of linguistic variable [19]. They are input, output, and intermediate variables where input variables (e.g. )…”

Section: B Multistage Fuzzy Rulesmentioning

confidence: 99%

Section: Rulementioning

confidence: 99%

See 1 more Smart Citation

On multistage fuzzy neural network modeling

Chung¹,

Duan

2000

IEEE Trans. Fuzzy Syst.

109

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“…There is a need for incorporating aspects of time and imprecision into real-time KMSs, considering appropriate semantic foundations (Bobrowitz, 1993;Chen & Parng, 1996;Lau et al 2008). In reality, it is a common practice for organizations to use one or more of the following (technical) systems and concepts to support their KM efforts (Binney, 2001;Wenger, 2001;Mazilescu, 2009b): Knowledge Maps, Taxonomies, Enterprise search engine, e-collaboration tools, Information repositories, Expert Systems, Data Mining / Knowledge Discovery systems, Case-based Reasoning / Question-Answering tools (for Helpdesk and/or Contact Centers), E-Learning and/or Learning Management Systems (LMS), Enterprise Information Portal, Intellectual Capital (IC) measurement tools.…”

Section: Related Workmentioning

confidence: 99%

A Knowledge Management System Embedded in the New Semantic Technologies

Mazilescu¹

2012

New Research on Knowledge Management Technology

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“…Second, it is able to minimize, simulate and verify not only a single multivalued relation but also a network of such relations. This is a very promising aspect, since the interest in systems composed of multiple and interconnected FRBSs is growing [15] [16] and their complexity reduction is a challenging research field. We show the results of the application of our approach to six public available classification benchmarks.…”

Section: Introductionmentioning

confidence: 99%

Complexity reduction of Mamdani Fuzzy Systems through multi-valued logic minimization

Cococcioni

Lazzerini

Marcelloni

2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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Abstract-In this paper, we propose an approach to complexity reduction of Mamdani-type Fuzzy Rule-Based Systems (FRBSs) based on removing logical redundancies. We first generate an FRBS from data by applying a simplified version of the well-known Wang and Mendel method. Then, we represent the FRBS as a multi-valued logic relation. Finally, we apply MVSIS, a tool for circuit minimization and simulation, to minimize the relation and consequently to reduce complexity of the associated FRBS. Unlike similar previous approaches proposed in the literature, the use of MVSIS let us deal with nondeterminism, that is, let us manage rules with the same antecedent but different consequents. To allow nondeterminism guarantees to achieve a higher (or at least not worse) complexity reduction than the one achievable from removing nondeterminism as soon as it appears. We apply our approach to six popular benchmarks. Results show a considerable complexity reduction associated only sporadically with consistent accuracy degradation. Moreover, quite surprisingly, the complexity reduction often comes together with an improvement in the classification accuracy.

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A new approach of multi-stage fuzzy logic inference

Cited by 23 publications

References 16 publications

On multistage fuzzy neural network modeling

On multistage fuzzy neural network modeling

A Knowledge Management System Embedded in the New Semantic Technologies

Complexity reduction of Mamdani Fuzzy Systems through multi-valued logic minimization

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