Fuzzy neural network methods have been successfully used to diagnose many diseases. This paper uses logic-based fuzzy neural networks to diagnose breast cancer. Logic-based fuzzy neural networks can select reduced size of input subspace by selecting useful inputs. For the optimization of the input subspace and the structure of the logic-based fuzzy neural networks, genetic algorithms and gradie...

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...

In this paper we present a new simple algorithm to construct Radial Basis Function (RBF) neural net based classifier. This algorithm has the major advantage to require nothing else that the training set to work (no step learning, threshold or other parameters like in other methods). Despite its simplicity, we show, on many benchmark datasets, that this algorithm provides a robust and efficient ...

Estimation of distribution algorithm for optimization of neural networks for intrusion detection system p. 9 Neural network implementation in reprogrammable FPGA devices-an example for MLP p. 19 A new approach for finding an optimal solution and regularization by learning dynamic momentum p. 29 Domain dynamics in optimization tasks p. 37 Nonlinear function learning by the normalized radial basi...

In this paper we present an approach for generation and initialization of fuzzy neural networks (FNN) from data. Fuzzy neural networks are concept that integrates some features of the fuzzy logic and the artificial neural networks theory. Based on analysis of several different fuzzy neural networks models, uniform representation method is presented, and two basic types are identified: FNN based...

Over the last decade or so, significant advances have been made in two distinct technological areas: fuzzy logic and computational neutral networks. The theory of fuzzy logic provides a mathematical framework to capture the uncertainties associated with human cognitive processes, such as thinking and reasoning. Also, it provides a mathematical morphology to emulate certain perceptual and lingui...

U-Net++ is one of the most prominent deep convolutional neural networks in field medical image segmentation after U-Net. However, semantic gaps between encoder and decoder subnets are still large, which will lead to fuzzy feature maps even target regions segmentation. To solve this problem, we propose an improved model utilizing channel attention mechanism Laplacian sharpening filter, SCU-Net++...

This study aims to develop an Adaptive Network-based Fuzzy Inference System technique (ANFIS) and using the parameters of a complex mathematical model in the RO membrane performances. The ANFIS was constructed by using a subtractive clustering method to generate initial fuzzy inference systems. The model trained by 70% of the data set and then its validity is examined by remained 30% data set. ...

The paper considers both knowledge acquisition and knowledge interpretation tasks as tightly connected and continuously interacting processes in a contemporary knowledge engineering system. Fuzzy rules are used here as a framework for knowledge representation. An algorithm REFuNN for fuzzy rules extraction from adaptive fuzzy neural networks (FuNN) is proposed. A case study of Iris classificati...