Displaying similar documents to “The estimation of electric power losses in electrical networks by fuzzy regression model using genetic algorithm.”

Learning fuzzy systems. An objective function-approach.

Frank Höppner, Frank Klawonn (2004)

Mathware and Soft Computing

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One of the most important aspects of fuzzy systems is that they are easily understandable and interpretable. This property, however, does not come for free but poses some essential constraints on the parameters of a fuzzy system (like the linguistic terms), which are sometimes overlooked when learning fuzzy system autornatically from data. In this paper, an objective function-based approach to learn fuzzy systems is developed, taking these constraints explicitly into account. Starting...

Neuro-fuzzy modelling based on a deterministic annealing approach

Robert Czabański (2005)

International Journal of Applied Mathematics and Computer Science

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This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty...

Computing with words and life data

Przemysław Grzegorzewski, Olgierd Hryniewicz (2002)

International Journal of Applied Mathematics and Computer Science

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The problem of statistical inference on the mean lifetime in the presence of vague data is considered. Situations with fuzzy lifetimes and an imprecise number of failures are discussed.

Stock price forecasting: Autoregressive modelling and fuzzy neural network.

Dusan Marcek (2000)

Mathware and Soft Computing

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Most models for the time series of stock prices have centered on autoregresive (AR) processes. Traditionaly, fundamental Box-Jenkins analysis [3] have been the mainstream methodology used to develop time series models. Next, we briefly describe the develop a classical AR model for stock price forecasting. Then a fuzzy regression model is then introduced. Following this description, an artificial fuzzy neural network based on B-spline member ship function is presented as an alternative...

Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set

Yaya Liu, Keyun Qin, Chang Rao, Mahamuda Alhaji Mahamadu (2017)

International Journal of Applied Mathematics and Computer Science

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The research on incomplete fuzzy soft sets is an integral part of the research on fuzzy soft sets and has been initiated recently. In this work, we first point out that an existing approach to predicting unknown data in an incomplete fuzzy soft set suffers from some limitations and then we propose an improved method. The hidden information between both objects and parameters revealed in our approach is more comprehensive. Furthermore, based on the similarity measures of fuzzy sets, a...

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...

An ε-insensitive approach to fuzzy clustering

Jacek Łęski (2001)

International Journal of Applied Mathematics and Computer Science

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Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians...