Experimental software datasets describing software projects in terms of their complexity and development time have been the subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such approaches as neural networks, fuzzy, and fuzzy neural network models. In this study, we introduce the concept of the Rule-based fuzzy polynomia...

in this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.the proposed method combines the fuzzy c-means clustering method, a recurrent functional neural fuzzy network (rfnfn), and a modified differential evolution.the proposed rfnfn is based on the two backlight factors that can accurately detect the compensat...

In order to reduce the memory footprint and energy consumption of embedded microcontroller in mobile robot, the concise differential evolution algorithm based on chaotic local search (CDE-CLS) is proposed for online optimization of recurrent fuzzy neural network (RFNN) controller in robot path planning so that the robot can be adaptive real-time obstacle avoidance. The CDE-CLS algorithm reduces...

Based on the electromagnetism-like algorithm (EM), we propose a novel hybrid learning algorithms which is the improved EM algorithm with genetic algorithm technique (IEMGA) for recurrent fuzzy neural system design. IEMGA are composed of initialization, local search, total force calculation, movement, and evaluation. They are hybridization of EM and GA. EM algorithm is a population-based meta-he...

This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are forme...

Based on previous work on encoding deterministic nite-state automata (DFAs) in discrete-time, second-order recurrent neural networks with sigmoidal discriminant functions, we propose an algorithm that constructs an augmented recurrent neural network that encodes fuzzy nite-state automata (FFAs). Given an arbitrary FFA, we apply an algorithm which transforms the FFA into an equivalent determinis...

A novel recurrent neurofuzzy network is proposed in this paper. More specifically, in this work we generalize the recurrent neurofuzzy network structure proposed in [1], which is in turn is an improvement of the feedforward structure introduced in [2]. The network structure is composed by two structures: a fuzzy inference system and a neural network. The fuzzy inference system contains fuzzy ne...

In this paper, a new architecture combining dynamic neural units and fuzzy logic approaches is proposed for a complex chemical process modeling. Such processes need a particular care where the designer constructs the neural network, the fuzzy and the fuzzy neural network models which are very useful in black box modeling. The proposed architecture is specified to the pH chemical reactor due to ...

in this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and newton-cotesmethods with positive coefficient for the solution of linear fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. here neural network isconsidered as a part of large field called neural computing orsoft computing. we propose alearning algorithm from ...

The paper proposed a new fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear mechanical plants with backlash. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive control systems design. The designed local control laws are coordinated by a fuzzy rule based control system. Sim...