Electrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this present paper, we develop an adaptive neuro-fuzzy inference system (ANFIS) model for groundwater electrical ...

We designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three ‘swing phase objectives’, characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller and a neural network, which makes the controller self tuning and adaptive. Two experiments have been perf...

Preliminary note The main goal of each technologist is the prediction of technological parameters by fulfilling the set design and technological demands. The work of the technologist is made easier by acquired knowledge and previous experience. A plan of input-output data was made by using the hybrid system of modelling ANFIS (Adaptive Neuro-Fuzzy Inference System) based on the results of seam ...

Accurate and robust estimation of applied forces in Robotic-Assisted Minimally Invasive Surgery is a very challenging task. Many vision-based solutions attempt to estimate the force by measuring the surface deformation after contacting the surgical tool. However, visual uncertainty, due to tool occlusion, is a major concern and can highly affect the results’ precision. In this paper, a novel de...

This paper describes combined approaches of data preparation, neural network analysis, and fuzzy inferencing techniques (which we collectively call neuro-fuzzy engineering) to the problem of environmental modelling. The overall neuro-fuzzy architecture is presented, and specific issues associated with environmental modelling are discussed. A case study that shows how these techniques can be com...

Models based on neural and neuro-fuzzy structures are developed to represent knowledge about a large data set containing chemical descriptors of organic compounds, commonly used in industrial processes. The neuro-fuzzy models here proposed include both, QSARs and original numerical values. The developed approaches use various techniques to insert knowledge by training, and to map rules in neuro...

Fuzzy systems can be used for function approximation based on a set of linguistic rules. We present a method to obtain the necessary parameters for such a fuzzy system by a neuro-fuzzy training method. The learning algorithm is able to determine the structure and the parameters of a fuzzy system from sample data. The approach is an extension to our already published NE-FCON and NEFCLASS models ...

This paper presents an innovative approach to self-adaptation of the structure of a neuro-fuzzy controller in real time. Without any off-line pretraining, the algorithm achieves very high control performance through the iteration of a three-stage algorithm. In the first stage, coarse tuning of the neuro-fuzzy rules (both rule consequents and membership functions of the premises) is accomplished...

Problem statement: We present development of neural network based fuzzy inference system for scheduling of parallel Jobs with the help of a real life workload data. The performance evaluation of a parallel system mainly depends on how the processes are co scheduled? Various co scheduling techniques available are First Come First Served, Gang Scheduling, Flexible Co Scheduling and Agile Algorith...

A photovoltaic (PV) panel produces energy that is influenced by external factors including temperature, irradiation, and the fluctuations in load related to it. The PV system should perform at maximum power point (MPP) order adjust towards rapidly increasing interest energy. Because of changing climatic conditions, it becomes has a limited efficiency. In maximize system's efficiency, technique ...