Time series prediction has been studied for decades due to its potential in a wide range of applications. As one the most popular technical indicators, moving average summarizes overall changing patterns over past period and is frequently used predict future trend time series. However, traditional indicators are calculated by averaging data with equal or predefined weights, ignore subtle differ...

Abstract: In this paper we discuss a two model multilayer neural network controller for adaptive control of blood pressure using sodium nitroprusside. A model with auto-regressive moving average, represent the dynamics of the system and a modified backpropagation training algorithm are used to design the control system to meet specified objectives of design and clinical constraints. Controller ...

Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of a Mexican river. Surprising results showed that in a monthly basis, ARIMA has lower prediction errors than this Neural Network. Key-Words: Auto re...

This paper presents a self-organizing power system stabilizer (SOPSS) which use the fuzzy Auto-Regressive Moving Average (FARMA) model. The control rules and the membership functions of proposed the fuzzy logic controller are generated automatically without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. To show t...

Recently, there are much works on developing models suitable for analyzing the volatility of a discrete-time process. Within the framework of Auto-Regressive Moving-Average (ARMA) processes, we derive a necessary and sufficient condition for the kernel to be non-negative. This condition is in terms of the generating function of the ARMA kernel which has a simple form. We discuss some useful con...

A time series forecasting is an active research applied significantly in a variety of economics areas. Over the past three decades an auto-regressive integrated moving average (ARIMA) model, as one of the most important time series models, has been applied in financial markets forecasting. Recent researches in time series forecasting ARIMA models indicate some basic limitations which detract fr...

In the past few years, increase in relation between physical and digital world over internet was witnessed. Even though applications can enhance smart home systems, it is still early stages challenges field of things (IoT). An extreme level data quality (DQ) system management essential to produce a meaningful vision. However, most energy has no straightforward process removing abnormal data. He...

Utilizing capacitor banks is very conventional in distribution network in order for local compensation of reactive power. This will be more important considering uncertainties including wind generation and loads uncertainty. Harmonics and non-linear loads are other challenges in power system which complicates the capacitor placement problem. Thus, uncertainty and network harmonics have been con...