This paper is concerned with the design of automated vehicle guidance control. First, we propose to implement the guidance tasks using several individual controllers. Next, a neural fuzzy network (NFN) is used to build these controllers, where the NFN constructs are neural-network-based connectionist models. A two-phase hybrid learning algorithm which combines genetic and gradient algorithms is...

This article presents the Neo-Fuzzy-Neuron Modified by Kohonen Network (NFN-MK), an hybrid computational model that combines fuzzy system techniques and artificial neural networks. Its main task consists in the automatic generation of membership functions, in particular, triangle forms, aiming a dynamic modeling of a system. The model is tested by simulating real systems, here represented by a ...

This study developed a neural fuzzy network (NFN) model with evolutionary learning algorithm for use in the field of food mycology for predicting growth in foodborne fungi. The evolutionary learning algorithm in the proposed model is a hybrid Taguchi-genetic algorithm (HTGA) that simultaneously finds the optimal antecedent and consequent parameters by directly minimizing root-mean-squared error...

Autonomous Underwater Vehicles (AUVs) have gained importance over the years as specialized tools for performing various underwater missions in military and civilian operations. This study presents the on-line system identification of AUV dynamics to obtain the coupled nonlinear dynamic model of AUV. This proposed model has an input-output relationship based upon neural fuzzy network (NFN) model...

The Cerebellar Model Arithmetic Controller (CMAC) is an intelligent controller like neural networks. Different form neural networks, CMAC can be regarded as one kind of “table-look-up” learning. Research shows that by including the fuzzy concept into the cell structure of CMAC, the accuracy can be significantly improved. Such an approach is called Fuzzy CMAC (FCMAC). In this study, it will be s...

The paper presents a fuzzy least squares support vector machine (LS-FSVM) which is implemented with the help of neo-fuzzy neurons (NFN) and which is essentially a zero order Takagi-Sugeno fuzzy inference system. The proposed LS-FSVM-NFN is numerically simple because it’s generated with NFNs, it also has a small number of adjustable parameters and high speed associated with the possibility of ap...

A hardware implementation of the neo-fuzzy neuron with the learning mechanism by the analog technology and its application to the on-board real-time prediction of time series are described. A neo-fuzzy neuron (NFN) is proposed for a learning machine of non-linear relations and dynamics. The NFN is produced by a fusion of the fuzzy logic and the neuroscience. The NFN describes the non-linearity ...

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired durin...

Electric load forecasting is essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather in...

Abstract--In this study, we introduce a concept of self-organizing neurofuzzy networks (SONFN), a hybrid modeling architecture combining relation-based neurofuzzy networks (NFN) and self-organizing polynomial neural networks (PNN). For such networks we develop a comprehensive design methodology and carry out a series of numeric experiments using data coming from the area of software engineering...