Neuro-Fuzzy Expert Systems: Overview with a Case Study

Mitra, Sukanya; Pal, Sankar K.

doi:10.1007/978-0-585-34652-6_6

Cited by 4 publications

(4 citation statements)

References 43 publications

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“…This leads to the design of fuzzy connectionist expert systems [19, 201. A study of neuro-fuzzy expert systems may be found in [21].…”

Section: Inferencing In the Fuzzy Expert System Modelmentioning

confidence: 99%

“…The neuron i,, selected for clause generation by (19)(20) can, however, result in feature U,, corresponding to any of the three properties low, medium or high by (21). This is because the path generated during backtracking is primarily determined by the connection weight magnitudes encoded during training.…”

Section: Justificationmentioning

confidence: 99%

“…Table I11 shows the rules generated from the knowledge base by presenting a sample set of test patterns. The antecedent parts are obtained using (19)(20)(21) while the consequent parts are deduced from the values of the certainty measure bel:.…”

Section: A Vowel Datamentioning

confidence: 99%

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Fuzzy multi-layer perceptron, inferencing and rule generation

Mitra

Pal

1995

IEEE Trans. Neural Netw.

167

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Abstmct-A connectionist expert system model, based on a fuzzy version of the multilayer perceptron developed by the authors, is proposed. It infers the output class membership value(s) of an input pattern and also generates a measure of certainty expressing confidence in the decision. The model is capable of querying the user for the mure important input feature information, if and when required, in case of partial inputs. Justification for an inferred decision may be produced in rule form, when so desired by the user. The magnitudes of the connection weights of the trained neural network are utilized in every stage of the proposed inferencing procedure. The antecedent and consequent parts of the justificatory rules are provided in natural forms. The effectiveness of the algorithm is tested on the speech recognition problem, on some medical data and on artificially generated intractable (linearly nonseparable) pattern classes.

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“…This leads to the design of fuzzy connectionist expert systems [19, 201. A study of neuro-fuzzy expert systems may be found in [21].…”

Section: Inferencing In the Fuzzy Expert System Modelmentioning

confidence: 99%

Section: Justificationmentioning

confidence: 99%

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Fuzzy multi-layer perceptron, inferencing and rule generation

Mitra

Pal

1995

IEEE Trans. Neural Netw.

167

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“…A natural linkage between linguistic variables and numerical values exists in fuzzy logic or fuzzy neural systems. Unfortunately the second of those requires a huge training data set, and the results of learning are not always reliable [24,25]. Consequently, the fuzzy logic expert system approach was selected to develop the control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fixed Wing Aircraft Automatic Landing with the Use of a Dedicated Ground Sign System

et al. 2021

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The paper presents automatic control of an aircraft in the longitudinal channel during automatic landing. There are two crucial components of the system presented in the paper: a vision system and an automatic landing system. The vision system processes pictures of dedicated on-ground signs which appear to an on-board video camera to determine a glide path. Image processing algorithms used by the system were implemented into an embedded system and tested under laboratory conditions according to the hardware-in-the-loop method. An output from the vision system was used as one of the input signals to an automatic landing system. The major components are control algorithms based on the fuzzy logic expert system. They were created to imitate pilot actions while landing the aircraft. Both systems were connected with one another for cooperation and to control an aircraft model in a simulation environment. Selected results of tests presenting control efficiency and precision are shown in the final section of the paper.

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Fuzzy MLP based expert system for medical diagnosis

Mitra

1994

Fuzzy Sets and Systems

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Neuro-Fuzzy Expert Systems: Overview with a Case Study

Cited by 4 publications

References 43 publications

Fuzzy multi-layer perceptron, inferencing and rule generation

Fuzzy multi-layer perceptron, inferencing and rule generation

Fixed Wing Aircraft Automatic Landing with the Use of a Dedicated Ground Sign System

Fuzzy MLP based expert system for medical diagnosis

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