This paper investigates the identification of nonlinear systems by utilizing soft-computing approaches. As the identification methods, Feedforward Neural Network architecture (FNN), Radial Basis Function Neural Networks (RBFNN), Runge-Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparative...

Unlike conventional sensor networks, wireless sensors are limited in power, have much smaller memory buffers, and possess relatively slower processing speeds. These characteristics necessitate minimum transfer and storage of information in order to prolong the network lifetime. In this paper, we exploit the spatiotemporal nature of sensor data to approximate the current values of the sensors ba...

The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken attention in environment of limited hydrological, meteorological, geological accuracy such depends on various parameters, including quality inputs ou...

Over the last few years there has been tremendous growth in the field of healthcare monitoring systems in hospitals and outside of it. Developing wireless health care monitoring devices employing various technologies has become a keen area of interest in India and as well as in other Nations. This proposed work aims to integrate artificial neural intelligence in domain of healthcare monitoring....

The paper deals with the problem of developing a multi-output soft sensor for industrial reactive distillation process methyl tert-butyl ether production. Unlike existing approaches, this proposes using filters to predict model errors, which are then taken into account as corrections in final predictions outputs. decomposition optimal estimation time delays is proposed each input sensor. Using ...

In recent years, soft computing and artificial intelligence techniques such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been effectively used in various civil engineering applications. This study aims to examine the potential of ANN and ANFIS for modeling the compressive strength of concrete containing expanded perlite powder (EPP). For doing this, ...

Artificial intelligence (AI) algorithms of adaptive neuro-fuzzy inference system or the network-based fuzzy (ANFIS) has been deployed to predict swelling potential (SP) treated weak soil. The soil was with quicklime activated rice husk ash (QARHA) and prediction efficiency compared previous outcomes this operation from literature. need for effective utilization construction materials achieve su...

The decision tree is one of the earliest predictive models in machine learning. In the soft decision tree, based on the hierarchical mixture of experts model, internal binary nodes take soft decisions and choose both children with probabilities given by a sigmoid gating function. Hence for an input, all the paths to all the leaves are traversed and all those leaves contribute to the final decis...