Role of Different Fuzzy Min- Max Neural Network for Pattern Classification
نویسنده
چکیده
Different neural networks related to Fuzzy min-max (FMM) has been studied and amongst all, Enhanced Fuzzy min-max (EFMM) neural network is most recent. For classification of patterns a new Enhanced Fuzzy Min-Max (EFMM) algorithm has been studied. The aim of EFMM is to improve the performance and minimize the restrictions that are possessed by original fuzzy min-max (FMM) network. Three heuristic rules are used to improve the learning algorithm of FMM. First, to eliminate the problem of overlapping during hyperbox expansion, new overlapping rules has been suggested. Second, to discover other overlapping cases the hyperbox test rule has been extended. Third, to resolve the hyperbox overlapping cases, hyperbox contraction rule is provided.
منابع مشابه
Fuzzy min-max neural networks - Part 2: Clustering
In an earlier companion paper [56] a supervised learning neural network pattern classifier called the fuzzy min-max classification neural network was described. In this sequel, the unsupervised learning pattern clustering sibling called the fuzzy min-max clustering neural network is presented. Pattern clusters are implemented here as fuzzy sets using a membership function with a hyperbox core t...
متن کاملAn Improved Fuzzy Neural Network for Solving Uncertainty in Pattern Classification and Identification
Dealing with uncertainty is one of the most critical problems in complicatedpattern recognition subjects. In this paper, we modify the structure of a useful UnsupervisedFuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types offuzzy neurons and its associated self organizing supervised learning algorithm. Thisimproved five-layer feed forward Supervised Fuzzy Neural Netwo...
متن کاملGeneral fuzzy min-max neural network for clustering and classification
This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms developed by Simpson. The GFMM method combines the supervised and unsupervised learning within a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering...
متن کاملA Modified Fuzzy Min-Max Neural Network and Its Application to Fault Classification
The Fuzzy Min-Max (FMM) network is a supervised neural network classifier that forms hyperbox fuzzy sets for learning and classification. In this paper, we propose modifications to FMM in an attempt to improve its classification performance in situations when large hyperboxes are formed by the network. To achieve the goal, the Euclidean distance is computed after network training. We also propo...
متن کاملEnhancing Performance of Relational Fuzzy Neural Networks with Square BK-Products
In this paper, we extend research done in max-min fuzzy neural networks in several important ways. We replace max and min operations used in the fuzzy operations by more general t-norms and co-norms, respectively. In addition, instead of the Łukasiewicz equivalence connective used in network of ReyesGarcia and Bandler, we employ in our hybridization a variety of equivalence connectives. We expl...
متن کامل