Displaying similar documents to “A model of route lifetime optimization with linguistic knowledge in wireless ad-hoc networks.”

p-Median problems in a fuzzy environment.

David Kutangila-Mayoya, José Luis Verdegay (2005)

Mathware and Soft Computing

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In this paper a formulation for the fuzzy p-median model in a fuzzy environment is presented. The model allows to find optimal locations of p facilities and their related cost when data related to the node demands and the edge distances are imprecise and uncertain and also to know the degree of certainty of the solution. For the sake of illustration, the proposed model is applied in a reduced map of Kinshasa (Democratic Republic of Congo) obtaining results which are rather than realistic...

Neural methods for obtaining fuzzy rules.

José Manuel Benítez, Armando Blanco, Miguel Delgado, Ignacio Requena (1996)

Mathware and Soft Computing

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In previous papers, we presented an empirical methodology based on Neural Networks for obtaining fuzzy rules which allow a system to be described, using a set of examples with the corresponding inputs and outputs. Now that the previous results have been completed, we present another procedure for obtaining fuzzy rules, also based on Neural Networks with Backpropagation, with no need to establish beforehand the labels or values of the variables that govern the system.

Rule weights in a neuro-fuzzy system with a hierarchical domain partition

Krzysztof Simiński (2010)

International Journal of Applied Mathematics and Computer Science

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The paper discusses the problem of rule weight tuning in neuro-fuzzy systems with parameterized consequences in which rule weights and the activation of the rules are not interchangeable. Some heuristic methods of rule weight computation in neuro-fuzzy systems with a hierarchical input domain partition and parameterized consequences are proposed. Several heuristics with experimental results showing the advantage of their usage are presented.

A methodology for constructing fuzzy rule-based classification systems.

José María Fernández Garrido, Ignacio Requena Ramos (2000)

Mathware and Soft Computing

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In this paper, a methodology to obtain a set of fuzzy rules for classification systems is presented. The system is represented in a layered fuzzy network, in which the links from input to hidden nodes represents the antecedents of the rules, and the consequents are represented by links from hidden to output nodes. Specific genetic algorithms are used in two phases to extract the rules. In the first phase an initial version of the rules is extracted, and in second one, the labels are...

Analyzing the reasoning mechanisms in fuzzy rule based classification systems.

Oscar Cordón, María José del Jesús, Francisco Herrera (1998)

Mathware and Soft Computing

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Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we do not consider the information provided by the other rules that are also compatible (have also been fired) with this example. In this paper we analyze this problem and propose...

Fuzzy systems and neural networks XML schemas for Soft Computing.

Adolfo Rodríguez de Soto, Conrado Andreu Capdevila, E. C. Fernández (2003)

Mathware and Soft Computing

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This article presents an XML[2] based language for the specification of objects in the Soft Computing area. The design promotes reuse and takes a compositional approach in which more complex constructs are built from simpler ones; it is also independent of implementation details as the definition of the language only states the expected behaviour of every possible implementation. Here the basic structures for the specification of concepts in the Fuzzy Logic area are described and a simple...

Fuzzy neural network approach to fuzzy polynomials.

Saeid Abbasbandy, M. Otadi (2006)

Mathware and Soft Computing

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In this paper, an architecture of fuzzy neural networks is proposed to find a real root of a dual fuzzy polynomial (if exists) by introducing a learning algorithm. We proposed a learning algorithm from the cost function for adjusting of crisp weights. According to fuzzy arithmetic, dual fuzzy polynomials can not be replaced by a fuzzy polynomials, directly. Finally, we illustrate our approach by numerical examples.

Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence

Chunshien Li, Tai-Wei Chiang (2012)

International Journal of Applied Mathematics and Computer Science

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Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions...