Displaying similar documents to “Fuzzy systems and neural networks XML schemas for Soft Computing.”

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.

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...

A methodology for developing knowledge-based systems.

Juan Luis Castro, José Jesús Castro-Sánchez, Antonio Espin, José Manuel Zurita (1998)

Mathware and Soft Computing

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This paper presents a methodology for developing fuzzy knowledge based systems (KBS), which permits a complete automatization. This methodology will be useful for approaching more complex problems that those in which machine learning from examples are successful.

A neuro-fuzzy system for isolated hand-written digit recognition.

Miguel Pinzolas, José Javier Astrain, Jesús Villadangos, José Ramón González de Mendívil (2001)

Mathware and Soft Computing

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A neuro-fuzzy system for isolated hand-written digit recognition using a similarity fuzzy measure is presented. The system is composed of two main blocks: a first block that normalizes the input and compares it with a set of fuzzy patterns, and a second block with a multilayer perceptron to perform a neuronal classification. The comparison with the fuzzy patterns is performed via a fuzzy similarity measure that uses the Yager parametric t-norms and t-conorms. Along this work, several...

An specification language for fuzzy systems.

Francisco José Moreno-Velo, Santiago Sánchez-Solano, Angel Barriga, M.ª Iluminada Baturone, Diego R. López (2001)

Mathware and Soft Computing

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This work presents the main features of XFL3, a language for fuzzy system specification, which has been defined as the common description languaje for the tools forming the Xfuzzy 3.0 development environment. Its main advantages are its capability to admit user-defined membership functions, parametric operators, and linguistic hedges. A brief summary of the tools included in Xfuzzy 3.0 and an example illustrating the use of XFL3 are also included.

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...

Systolic architectures for fuzzy processing and their simulation.

Luis de Salvador, Marcos García, Julio Gutiérrez (1994)

Mathware and Soft Computing

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This paper details the study of systolic architectures for fuzzy rules processing made at the Hardware and Advanced Control Laboratory - INTA. The theoretical basis of these architectures is described and analysed. Likewise, the resultant schematics are simulated using a hardware description language (VHDL) with standard cells from ES2. This gives us a very accurate assessment of their real performance. In this way we can detect the inherent shortcomings in this class of systems and...

New aspects on extraction of fuzzy rules using neural networks.

José Manuel Benítez, Armando Blanco, Miguel Delgado, Ignacio Requena (1998)

Mathware and Soft Computing

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In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe the behaviour of a system. We have used Artificial Neural Netorks (ANN) with the Backpropagation algorithm, and a set of examples of the system. In this work, some modifications which allow to improve the results, by means of an adaptation or refinement of the variable labels in each rule, or the extraction of local rules using distributed ANN, are showed. An interesting application on the...

Fuzzy sets in computer vision: an overview.

Pilar Sobrevilla, Eduard Montseny (2003)

Mathware and Soft Computing

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Every computer vision level crawl with uncertainty, what makes its management a significant problem to be considered and solved when trying for automated systems for scene analysis and interpretation. This is why fuzzy set theory and fuzzy logic is making many inroads into the handling of uncertainty in various aspects of image processing and computer vision. The growth within the use of fuzzy set theory in computer vision is keeping pace with the use of more complex algorithms...