Fuzzy Technique for Order Preference by Similarly to Ideal Solution (TOPSIS) is one of the most commonly used approaches in solving numerous multiple criteria decision making problems. It has been widely used in ranking of multiple alternatives with respect to multiple criteria with the superiority of fuzzy set type-1 and subjective weights. Recently, fuzzy TOPSIS has been merged with interval ...

A method to extract a fuzzy rule based system from a trained artificial neural network for classification is presented. The fuzzy system obtained is equivalent to the corresponding neural network. In the antecedents of the fuzzy rules, it uses the similarity between the input datum and the weight vectors. This implies rules highly understandable. Thus, both the fuzzy system and a simple analysi...

The indicators system and indicators normalization method for the condition assessment of Transformer is developed and membership function of the indicators is established. The establishment of Expert Weight is decided jointly by subjective weight and objective weight which is based on entropy weight thoughts, while the indicators weight is gained by the weight which is derived from the standar...

Environmental Performance Index (EPI) has been introduced since 2006 to depict the environmental performance for most of the countries in the world. The index considers ten policy categories associated with environmental public health and ecosystem sustainability. The main mathematics operation in establishing EPI is arithmetic mean of all ten policy categories. However, this operation carries ...

With the development of RS technology, the evaluation of DOM becomes more important. Multi-level fuzzy comprehensive evaluation is the traditional method for DOM evaluation. But in the process of traditional multi-Level Fuzzy Comprehensive Assessment, the weight of evaluation factor which are subjective is always ascertained by experts. The method can cause evaluation bias because of the subjec...

In this contribution we carry out an analysis of the rule weights and Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of imbalanced data-sets with a high imbalance degree. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best configuration of rule weight and Fuzzy Reasoning Method also studying the cooperation of some...

This paper presents a novel improved fuzzy particle swarm optimization (IFPSO) algorithm to the intelligent identification and control of a dynamic system. The proposed algorithm estimates optimally the parameters of system and controller by minimizing the mean of squared errors. The particle swarm optimization is enhanced intelligently by using a fuzzy inertia weight to rationally balance the ...

In neural network the connection strength of each neuron is updated through learning. Through repeated simulations of crisp neural network, we propose the idea that for each neuron in the network, we can obtain reduced model with more efficiency using wavelet based multiresolution analysis (MRA) to form wavelet based quasi fuzzy weight sets (WBQFWS). Such type of WBQFWS provides good initial so...

This paper presents a new boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is built in an incremental fashion, in that the evolutionary algorithm extracts one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training instances...

This paper presents a novel boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training in...