Neurofuzzy Systems

Pedrycz, Witold; Kandel, Abraham; Zhang, Yanqing

doi:10.1007/978-1-4615-5505-6_10

Cited by 8 publications

(16 citation statements)

References 116 publications

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“…This can be done by prior knowledge (to predefine a membership function), by learning from sample data, or by both of these approaches (Nauck and Kruse 1998). When the option of learning from sample data is applied and the learning capability of neural networks is used, the approach is usually called a neurofuzzy system (Pedrycz, Kandel, and Zhang 1998). Neurofuzzy systems represent a particular class of the broad family of fuzzy neural networks.…”

Section: Neurofuzzy Proximity Modelingmentioning

confidence: 99%

“…It is often emphasized in the literature that fuzzy sets are focused on knowledge representation but cannot accommodate efficacies implied by the underlying data (Pedrycz, Kandel, and Zhang 1998). On the other hand, neural networks are powerful at learning from underlying data but poor in representing existing knowledge.…”

Section: Neurofuzzy Proximity Modelingmentioning

confidence: 99%

“…In sum, a major difference between fuzzy and neurofuzzy systems is that fuzzy systems acquire knowledge of parameters (of fuzzy membership functions and fuzzy rules) mostly from expert knowledge while neurofuzzy systems obtain it from training data (Pedrycz, Kandel, and Zhang 1998). We will use neurofuzzy inference to acquire implicitly the proximity fuzzy membership functions through existing sample cases (known as training cases).…”

Section: Neurofuzzy Proximity Modelingmentioning

confidence: 99%

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Neurofuzzy Modeling of Context–Contingent Proximity Relations

Yao

Thill

2007

Geographical Analysis

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The notion of proximity is one of the foundational elements in humans' understanding and reasoning of the geographical environments. The perception and cognition of distances plays a significant role in many daily human activities. Yet, few studies have thus far provided context–contingent translation mechanisms between linguistic proximity descriptors (e.g., “near,”“far”) and metric distance measures. One problem with previous fuzzy logic proximity modeling studies is that they presume the form of the fuzzy membership functions of proximity relations. Another problem is that previous studies have fundamental weaknesses in considering context factors in proximity models. We argue that statistical approaches are ill suited to proximity modeling because of the inherently fuzzy nature of the relations between linguistic and metric distance measures. In this study, we propose a neurofuzzy system approach to solve this problem. The approach allows for the dynamic construction of context–contingent proximity models based on sample data. An empirical case study with human subject survey data is carried out to test the validity of the approach and to compare it with the previous statistical approach. Interpretation and prediction accuracy of the empirical study are discussed.

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Section: Neurofuzzy Proximity Modelingmentioning

confidence: 99%

Section: Neurofuzzy Proximity Modelingmentioning

confidence: 99%

Section: Neurofuzzy Proximity Modelingmentioning

confidence: 99%

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Neurofuzzy Modeling of Context–Contingent Proximity Relations

Yao

Thill

2007

Geographical Analysis

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“…The neurofuzzy system works similarly to that of mu lti-layer neural network. Th is hybrid system uses the adaptive neural networks (ANNs) theory to characterize the input-output relationship and build the fuzzy rules by determining the input structure [7]. Neuro-fu zzy systems exp loit the capacity of the two concepts, fuzzy logic theory and ANNs, by utilizing the values of parameters in the adaptive nodes of adaptive neural networks in tuning rule based system that approximate the functional relat ions between responses and input variables of the process under study.…”

Section: Introductionmentioning

confidence: 99%

“…In [7], we presented a new model for parameters identification using the FEM coupled with ANFIS system. The objective of this paper is to propose the new fast optimization method for solving inverse electro magnetic problem in electrical engineering such as the geometrical shape optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A New Application of an ANFIS for the Shape Optimal Design of Electromagnetic Devices

Mohdeb¹,

Hacib²

2014

IJISA

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Abstract-This paper presents a new model based on simulated annealing algorithm (ASA) and adaptive neuro-fuzzy inference system (ANFIS) for shape optimization and its applications to electromagnetic devices. The proposed model uses ANFIS system to evaluate the electromagnetic performance of the device. Both the ANFIS and ASA method are applied to the design/optimization of the electromagnetic actuator. The results of the proposed approach are compared with other techniques such as: method of moving asymptotes, penalty method, augmented lagrangian genetic algorithm and simulated annealing method (SA). Among the algorithms, the proposed ANFIS-ASA approach significantly outperforms the other methods.

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Non-Probabilistic Uncertainty in Subsurface Hydrology and Its Applications: an Overview

Ozbek

Pinder

2006

Water Air Soil Pollut: Focus

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While a presumed equality between uncertainty and probability is dominant in subsurface hydrology, in other areas of science and engineering progress in the mathematics of uncertainty is leading the way in providing new types of uncertainty, distinct from probability. In this paper our focus is on one of these, namely fuzzy set theory and fuzzy logic. We start with an overview of fuzzy theory introducing terminology, notation, and concepts relevant to our paper. We continue our discussion with an overview of currently known applications in several areas that include subsurface characterization, groundwater flow and transport modeling, water resources management and optimization, and groundwater health risk assessment and management.

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Neurofuzzy Systems

Cited by 8 publications

References 116 publications

Neurofuzzy Modeling of Context–Contingent Proximity Relations

Neurofuzzy Modeling of Context–Contingent Proximity Relations

A New Application of an ANFIS for the Shape Optimal Design of Electromagnetic Devices

Non-Probabilistic Uncertainty in Subsurface Hydrology and Its Applications: an Overview

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