Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A multifaceted perspective at data analysis: a study in collaborative intelligent agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
IEEE Transactions on Neural Networks
Rough Set Based Generalized Fuzzy -Means Algorithm and Quantitative Indices
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The Development of Incremental Models
IEEE Transactions on Fuzzy Systems
Relation-based neurofuzzy networks with evolutionary data granulation
Mathematical and Computer Modelling: An International Journal
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
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In this study, we introduce a new design methodology of granular-oriented self-organizing Hybrid Fuzzy polynomial neural networks (HFPNN) that is based on multi-layer perceptron with context-based polynomial neurons (CPNs) or polynomial neurons (PNs). In contrast to the typical architectures encountered in polynomial neural networks (PNN), our main objective is to develop a design strategy of HFPNN as follows: (a) The first layer of the proposed network consists of context-based polynomial neuron (CPN). Here CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of context-based fuzzy c-means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data (input variables) while the formation of the clusters here is guided by a collection of some predefined fuzzy sets (so-called contexts) specified in the output space. (b) The proposed design procedure being applied to each layer of HFPNN leads to the selection of the preferred nodes of the network (CPNs or PNs) whose local characteristics (such as the number of contexts, the number of clusters, a collection of the specific subset of input variables, and the order of the polynomial) can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of the performance of the proposed HFPNN, we use well-known machine learning data coming from the machine learning repository.