Stock price forecasting models with lower Mean Squared Error (MSE) are more “welcome" than those who have higher MSE. However, this “welcome" may disappear when lower MSE leads to an unexpected loss whereas higher MSE leads to a positive profit in a practical investment. The reason is that MSE aims to evaluate models in the view of “accuracy", which has nothing to do with profit. We therefore p...

Due to its extensive use for motion control systems in industry, tuning of the proportional-integral-derivative (PID) controller parameters has been the focus of intensive research. In this paper, a novel tuning method for the parameters of PID controller based speed control of DC motor using genetic algorithm (GA) is proposed. The main advantage of the proposed method is that the mathematical ...

Mean squared error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem. However, MSE gives biased results when the input signals are noisy. This paper presents a novel stochastic gradient algorithm based on the recently proposed error whitening criterion (EWC) to tackle the problem of linear filter estimation in the presence of additiv...

The problem of predicting oil prices is worthy of attention. As oil represents the backbone of the world economy, the goal of this paper is to design a model, which is more accurate. We modeled the prediction process comprising of three steps: feature selection, data partitioning and analyzing the prediction models. Six prediction models namely: Multi-Layered Perceptron (MLP), Sequential Minima...

This paper addresses an effective learning method that enables us to directly optimize neural network classifier's discrimination performance at a desired local operating range by maximizing a partial area under a receiver operating characteristic (ROC) or domain-specific curve, which is difficult to achieve with classification accuracy or mean squared error (MSE)-based learning methods. The ef...

Some ratio estimators for estimating the population mean of the variable under study, which make use of information regarding the population proportion possessing certain attribute, are proposed. Under simple random sampling without replacement (SRSWOR) scheme, the expressions of bias and mean-squared error (MSE) up to the first order of approximation are derived. The results obtained have been...

In sample surveys, it is usual to make use of auxiliary information to increase the precision of the estimators. We propose a new chain ratio estimator and regression estimator of a finite population mean using linear combination of two auxiliary variables and obtain the mean squared error (MSE) equations for the proposed estimators. We find theoretical conditions that make proposed estimators ...

In this paper, a partial least square regression (PLSR) and fuzzylogic based smart decision support system (DSS) for crop-specific irrigation notification and control in precision agriculture is proposed, and this can be implemented in farm land, green-house and poly-house. The proposed DSS model continuously acquires real-time soil and environmental data using Zigbee wireless sensor network (W...

Distributed hydrological models combine observations and knowledge about a hydrological system to make spatial predictions of hydrological attributes. These models require methods to assess their performance at spatial prediction. The current practice for assessment is simplistic. For qualitative assessment, simulated spatial patterns are compared visually against an observed pattern to assess ...