Fuzzy rule extraction from a multilayered neural network

Enbutsu, Ichiro; Baba, Kenji; Hara, Nobuhiro

doi:10.1109/ijcnn.1991.155377

Cited by 39 publications

(17 citation statements)

References 4 publications

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“…To equip a fuzzy system with learning capabilities, an alternative approach is to integrate fuzzy sets with other learning models. Lots of researchers have proposed the use of neural networks in fuzzy systems: Wong and Wang have proposed a FuzzyNet model [5], Enbutsu et al developed a hybridization scheme to extract fuzzy rules from a multilayered neural network [6], and Janikow suggests the use of a genetic algorithm for a learning fuzzy controller [7].…”

Section: Figure 1 Dangerous Detectingmentioning

confidence: 99%

The Research of Vehicle Driving Multimedia Data Recording

Meng

Sun

2008

2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing

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The paper is the research and application conclusion of audio/video and sensor data recording in vehicle driving. The system encodes the data with MP4 format and reduces the data saving with dangerous signal detecting. With the data saved in SD card, this design can realize fully digital solider recording and obtain highly reliability in working. The article discusses the different working mode can be selected by drivers. With multimedia playback ability, we fully used digital chips and processor add-in functions. This recorder research, designed in combination with data recording and multimedia playing in vehicle has most multimedia application valuable in the future.

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Section: Figure 1 Dangerous Detectingmentioning

confidence: 99%

The Research of Vehicle Driving Multimedia Data Recording

Meng

Sun

2008

2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing

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“…7b). The weight matrices between the input and the hidden layers, and between the hidden and the output layers respectively, are combined as follows [13,21]:…”

Section: The Interpretation Of Gene Expression Data By Fuzzy Rule Extmentioning

confidence: 99%

“…Relevant fuzzy rules can be extracted from IKMs using the effect measure method (EMM) [13] and added as EKM modules after a validation and (possible) refining process. EMM combines the weights between the layers of the network in order to select the strongest dependencies between the fuzzy output and inputs.…”

Section: The Interpretation Of Gene Expression Data By Fuzzy Rule Extmentioning

confidence: 99%

A neuro-fuzzy approach for functional genomics data interpretation and analysis

Neagu

Palade

2003

Neural Computing & Applications

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“…This is equivalent to finding proper input-space fuzzy clustering or, more precisely, to forming proper fuzzy hyperboxes in the input space. Initially, for each complement coded input vector [see (1)], the values of choice functions are computed by (11) where " " is the minimum operator performed for the pairwise elements of two vectors, is a constant, is the current number of rule nodes, and is the complement weight vector, which is defined by . Notice that is the weight vector of Layer-2 links associated with rule node .…”

Section: A the Structure Learning Stepmentioning

confidence: 99%

An ART-based fuzzy adaptive learning control network

Lin

1997

IEEE Trans. Fuzzy Syst.

186

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This paper addresses the structure and an associated on-line learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm called FALCON-ART is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The FALCON-ART has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids." As the number of input/output variables increases, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the FALCON-ART partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the FALCON-ART can create and train the FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or even any initial information on these. More notably, the FALCON-ART can on-line partition the input/output spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online incoming training data. Computer simulations have been conducted to illustrate the performance and applicability of the proposed system.

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Fuzzy rule extraction from a multilayered neural network

Cited by 39 publications

References 4 publications

The Research of Vehicle Driving Multimedia Data Recording

The Research of Vehicle Driving Multimedia Data Recording

A neuro-fuzzy approach for functional genomics data interpretation and analysis

An ART-based fuzzy adaptive learning control network

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