Dimensionality reduction is an important problem in pattern recognition. Reducing the dimensionality of feature can improve the effecitveness and efficiency of pattern recognition algorithms. Minimum Classification Error(MCE) training algorithm is a power tool for dimensionality resuction. However, MCE training process is a type of thorough search process for the local minimum, global minimum c...

The block motion estimation process is an integral and important part in the current video coding standards. However, the current algorithms for the block motion estimation process either have a large computational complexity, or have a poor accuracy. The UMHexagonS algorithm is recommended for the implementation of H.264 for its relatively good performance. In this paper, a new fast block moti...

accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. however, due to its time variant behavior and non-linear and non-stationary nature, electricity...

In block-matching motion estimation (BMME), the search patterns have a significant impact on the algorithm’s performance, both the search speed and the search quality. The search pattern should be designed to fit the motion vector probability (MVP) distribution characteristics of the real-world sequences. In this paper, we build a directional model of MVP distribution to describe the directiona...

this paper introduces a technique for controlling a class of uncertain chaotic systems using an adaptive fuzzy proportional-integrator-derivative (pid) controller with h∞ tracking performance. the purpose of this work is to achieve optimal tracking performance of the controller using backtracking search algorithm (bsa). bsa, which is a novel heuristic algorithm, has an easy structure with singl...

The non-convex behavior presented by nonlinear systems limits the application of classical optimization techniques to solve optimal control problems for these kinds of systems. This paper proposes a hybrid algorithm, namely BA-SD, by combining Bee algorithm (BA) with steepest descent (SD) method for numerically solving nonlinear optimal control (NOC) problems. The proposed algorithm includes th...

Solving real world Multi-Objective Optimization Problems (MOOP) often involves the use of complex “black-box ”modeling routines, where the resolution of the process governing equations requires the use of expensive numerical methods [1]. Multi-Objective Evolutionary Algorithms (MOEA) is an excellent tool to deal with the multi-objective nature of these problems. They do not need the calculation...

Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small mole...

in this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which the uncertainty is characterized by given finite numbers of scenarios. in this model, the objective function minimizes the total expected costs of transportation and opening facilities subject to the robustness constraint. to tackle the problem efficiently and effectively, an effici...