Extraction of similarity based fuzzy rules from artificial neural networks

Mantas, Carlos Javier; Puche, J. M.; Mantas, José M.

doi:10.1016/j.ijar.2006.04.003

Cited by 36 publications

(20 citation statements)

References 26 publications

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“…This problem is well known within the neural network community [18]. Many approaches have been proposed to deal with this problem, both generic approaches such as fuzzy rule extraction from neural networks [19] as well as domain specific approaches (i.e., visualizing weights of deep image recognition networks [20]). A technique to trace back decisions through the network to identify relevant patterns in the input sentence would certainly be important to real users, especially when incorporating our approach into a tool.…”

Section: A Limitations Of the Approachmentioning

confidence: 99%

Automatic Classification of Requirements Based on Convolutional Neural Networks

Winkler

Vogelsang

2016

2016 IEEE 24th International Requirements Engineering Conference Workshops (REW)

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Section: A Limitations Of the Approachmentioning

confidence: 99%

Automatic Classification of Requirements Based on Convolutional Neural Networks

Winkler

Vogelsang

2016

2016 IEEE 24th International Requirements Engineering Conference Workshops (REW)

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“…The application of (1) for inserting and extracting symbolic knowledge from feedforward ANNs was demonstrated [29,30] (for other approaches relating feedforward ANNs and FRBs, see [3,6,21,35]). …”

Section: The All Permutations Fuzzy Rule-basementioning

confidence: 99%

Extracting symbolic knowledge from recurrent neural networks—A fuzzy logic approach

Kolman

Margaliot

2009

Fuzzy Sets and Systems

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Considerable research has been devoted to the integration of fuzzy logic (FL) tools with classic artificial intelligence (AI) paradigms. One reason for this is that FL provides powerful mechanisms for handling and processing symbolic information stated using natural language. In this respect, fuzzy rule-based systems are white-boxes, as they process information in a form that is easy to understand, verify and, if necessary, refine.The synergy between artificial neural networks (ANNs), which are notorious for their black-box character, and FL proved to be particularly successful. Such a synergy allows combining the powerful learning-from-examples capability of ANNs with the high-level symbolic information processing of FL systems.In this paper, we present a new approach for extracting symbolic information from recurrent neural networks (RNNs). The approach is based on the mathematical equivalence between a specific fuzzy rulebase and functions composed of sums of sigmoids. We show that this equivalence can be used to provide a comprehensible explanation of the RNN functioning. We demonstrate the applicability of our approach by using it to extract the knowledge embedded within an RNN trained to recognize a formal language.

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“…) Summarizing, the counter is realized by (34) and (35). Note that these two equations can be interpreted as a secondorder RNN.…”

Section: B Knowledge-based Design: the Modular Approachmentioning

confidence: 99%

“…This was used to develop a new approach to knowledge-based computing in feedforward ANNs. For some other approaches relating ANNs and FRBs, see [5], [8], [21], [35].…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Knowledge-Based Design of Recurrent Neural Networks

Kolman

Margaliot

2008

IEEE Trans. Neural Netw.

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Abstract-A major drawback of artificial neural networks (ANNs) is their black-box character. This is especially true for recurrent neural networks (RNNs) because of their intricate feedback connections. In particular, given a problem and some initial information concerning its solution, it is not at all clear how to design an RNN that is suitable for solving this problem.In this paper, we consider a fuzzy rule-base with a special structure, referred to as the fuzzy all-permutations rulebase (FARB). Inferring the FARB yields an input-output mapping that is mathematically equivalent to that of an RNN.We use this equivalence to develop two new knowledge-based design methods for RNNs. The first method, referred to as the direct approach, is based on stating the desired functioning of the RNN in terms of several sets of symbolic rules, each one corresponding to a sub-network. Each set is then transformed into a suitable FARB.The second method is based on first using the direct approach to design a library of simple modules, such as counters or comparators, and realize them using RNNs. Once designed, the correctness of each RNN can be verified. Then, the initial design problem is solved by using these basic modules as building blocks. This yields a modular and systematic approach for knowledge-based design of RNNs. We demonstrate the efficiency of these approaches by designing RNNs that recognize both regular and non-regular formal languages.

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Extraction of similarity based fuzzy rules from artificial neural networks

Cited by 36 publications

References 26 publications

Automatic Classification of Requirements Based on Convolutional Neural Networks

Automatic Classification of Requirements Based on Convolutional Neural Networks

Extracting symbolic knowledge from recurrent neural networks—A fuzzy logic approach

A New Approach to Knowledge-Based Design of Recurrent Neural Networks

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