this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

Research shows that multi-fractal can not only exhibit the singular probability distribution form of the fractal signal completely, but also increase the fine level of signal geometrical characteristics and local scaling behavior. Based on multi fractal dimension calculation of time-frequency matrix of vibration signal of rolling bearing in this paper, energy distribution characteristics of tim...

The article presents possibilities of using different artificial neural networks and neuro-fuzzy systems to solve certain engineering geodesy tasks. Special attention is paid to tasks connected with the construction of a numerical terrain model, transformation of coordinates from the “1965” system into the “2000” system, and prediction of a time series on the basis of results of GPS measurement...

Function and its partial derivative approximation based upon a set of discrete dataset are important issues in soft computing. Several function approximators have been presented most of them fits a model to the dataset so that the Mean Squared Error is minimized. In this paper, we propose to calculate the derivative of the Neuro-Fuzzy function approximator directly according to the parametric s...

precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

In this paper, Artificial Neural Network, one of the Artificial Intelligence (AI) techniques, for the Volt / Var control in power distribution systems with dispersed generation (DG) is proposed. Artificial neural networks have been considered due to their ability for real time control, simpler calculations and adaptability to different operating conditions. Neuro-controllers are much more effec...

This paper presents an adaptive neuro-fuzzy controller ANFIS (Adaptive Neuro-Fuzzy Inference System) for a bilateral teleoperation system based on FPGA (Field Programmable Gate Array). The proposed controller combines the learning capabilities of neural networks with the inference capabilities of fuzzy logic, to adapt with dynamic variations in master and slave robots and to guarantee good prac...

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy archi...

The aim of this work is to study the applications of soft computing technique on the machinability of bagasse fiber-reinforced vinyl ester composites. The prediction accuracy of regression, artificial neural networks, and neuro-fuzzy methods on the response variables during machining of bagasse fiber-reinforced vinyl ester composite sheets.. In experimental part, a total of 27 experiments was c...

As demand for high quality transmission increases, improving spectrum efficiency and error performance in wireless communication systems are important. OFDM is a multi-carrier modulation technique with densely spaced sub-carriers that has gained a lot of popularity among the broadband community in the last few years. One of the promising approaches to next generation communication systems are a...