Comparison of crisp and fuzzy character networks in handwritten word recognition

Experiments involving handwritten word recognition on words taken from images of handwritten address blocks from the United States Postal Service mailstream are described. The word recognition algorithm relies on the use of neural networks at the character level. The neural networks are trained using crisp and fuzzy desired outputs. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level

Retail optimization in Romanian metallurgical industry by applying of fuzzy networks concept

Our article presents possibilities of applying the concept Fuzzy Networks for an efficient metallurgical industry in Romania. We also present and analyze Fuzzy Networks complementary concepts, such as Expert Systems (ES), Enterprise Resource Planning (ERP), Analytics and Intelligent Strategies (SAI). The main results of our article are based on a case study of the possibilities of applying these concepts in metallurgy through Fuzzy Networks. Also, it is presented a case study on the application of the FUZZY concept on the Romanian metallurgical industry

Метод структурно-параметричного синтезу нейро-фаззі мереж

Abstract – A method of structural parametric synthesis of neuro-fuzzy networks is developed. The proposed method uses decision trees to build a neuro-fuzzy networks, is not highly iterative and does not require the solution of multidimensional optimization task for network parameters calculation. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2880

A System for Analyzing and Processing Data on the Quantitative and Qualitative Characteristics of University Staff Based on the Apparatus of Soft Computing

Models and algorithms for optimizing the description, identification of non-stationary objects, analysis and synthesis of tasks of intelligent personnel management systems have been developed that have the properties inherent in neuro-fuzzy networks and provide a convenient interface for decision-making. Methods for forming fuzzy rule bases, modeling membership functions and linguistic terms, synthesizing components of neuro-fuzzy networks are proposed

Application possibilities of fuzzy networks concept in metallurgical industry

Our article presents possibilities of applying the concept Fuzzy Networks for an efficient metallurgical industry in Romania. We also present and analyze Fuzzy Networks complementary concepts, such as Expert Systems (ES), Enterprise Resource Planning (ERP), Analytics and Intelligent Strategies (SAI). The main results of our article are based on a case study of the possibilities of applying these concepts in metallurgy through Fuzzy Networks. There are presented the domains afferent to KIBS are defined complying with the standardized classification of industrial sectors according to European Monitoring Centre on Change (EMCC). It is also analyze important approaches in the specific scientific literature, the research methodology a study case. KIBS concept implementation to the Romanian metallurgical industry aims also to include performance of activities concerning the formulation and development of new specific concepts

Методы синтеза и модели нейро-нечетких сетей для решения задач диагностики и классификации по признакам

Проанализированы известные методы синтеза и модели нейро-нечетких сетей прямого распространения. Впервые предложены критерии, позволяющие оценивать временную и пространственную сложность неитеративных методов обучения нейро-нечетких сетей.Проаналізовано відомі методи синтезу і моделі нейро-нечітких мереж прямого поширення. Уперше запропоновано критерії, що дозволяють оцінювати часову і просторову складність неітеративних методів навчання нейро-нечітких мереж.The known methods of synthesis and models of feed-forward neuro-fuzzy networks are analyzed. The new criteria for measuring the temporal and spatial complexity of non-iterative training methods of neuro-fuzzy networks are proposed

Approximation properties of the neuro-fuzzy minimum function

The integration of fuzzy logic systems and neural networks in data driven nonlinear modeling applications has generally been limited to functions based upon the multiplicative fuzzy implication rule for theoretical and computational reasons. We derive a universal approximation result for the minimum fuzzy implication rule as well as a differentiable substitute function that allows fast optimization and function approximation with neuro-fuzzy networks. --Fuzzy Logic,Neural Networks,Nonlinear Modeling,Optimization

Matrix formulation of fuzzy rule-based systems

In this paper, a matrix formulation of fuzzy rule based systems is introduced. A gradient descent training algorithm for the determination of the unknown parameters can also be expressed in a matrix form for various adaptive fuzzy networks. When converting a rule-based system to the proposed matrix formulation, only three sets of linear/nonlinear equations are required instead of set of rules and an inference mechanism. There are a number of advantages which the matrix formulation has compared with the linguistic approach. Firstly, it obviates the differences among the various architectures; and secondly, it is much easier to organize data in the implementation or simulation of the fuzzy system. The formulation will be illustrated by a number of examples