A common derivation of principal component analysis (PCA) is based on the minimization of the squared-error between centered data and linear model, corresponding to the reconstruction error. In fact, minimizing the squared-error leads to principal subspace analysis where scaled and rotated principal axes of a set of observed data, are estimated. In this paper, we introduce and investigate an al...

We provide statistical performance guarantees for a recently introduced kernel classifier that optimizes the L2 or integrated squared error (ISE) of a difference of densities. The classifier is similar to a support vector machine (SVM) in that it is the solution of a quadratic program and yields a sparse classifier. Unlike SVMs, however, the L2 kernel classifier does not involve a regularizatio...

Micro particles have the potentials to be used for many medical purposes in-side human body such as drug delivery and other operations. This paper attempts provide a thorough comparison between five meta-heuristic search algorithms: Arithmetic optimization algorithm (AOA), Artificial Gorilla troop’s (GTO), Seagull (SOA), Parasitism-predation Algorithm (PPA), hybrid AOA GTO (HAOAGTO). These appr...

1. Problem formulation 2. Time-domain performance specifications 2.1. Transient Performance 2.2. Integral Criteria 2.3. Calculation of the ISE-Performance Index 3. Optimal controller settings subject to the ISE-criterion 3.1. Example 3.2. Optimal Settings for Combinations of n PT -Plants and Standard Controllers of PID Type 4. Empirical procedures 4.1. Tuning Rules for Standard Controllers 4.1....

This paper realized the 2-DOF controller structure for first order with time delay systems. The co-prime factorization is used to design observer based controller K(s), representing one degree of freedom. The problem is based on H∞ norm of mixed sensitivity and aims to achieve stability, robustness and disturbance rejection. Then, the other degree of freedom, prefilter F(s), is formulated as fi...

In this paper we consider the following problem: viewing both a nonlinear system model and its linearization as mappings from input-tostate, we define the error between the state of the original nonlinear system and that of the linearization and find the region of the state space where this error is norm-bounded, in the integral-square (or L2-norm) sense. Using the Hamilton–Jacobi inequality we...

Abstract The importance of safety in transportation has forced developments automated vehicles. Many control methods have been introduced to ensure safe distance between vehicle and another car. This article presents an adaptive cruise whose parameters are tuned by genetic algorithm for the case study vehicle. Two algorithms with two different objective functions, i.e., integral square error (I...

In this paper, an artificial intelligence method particle swarm optimization (PSO) algorithm is presented for determining the optimal PI controller parameters for the indirect control active and reactive power of doubly fed induction generator (DFIG) to ensure a maximum power point tracking of a wind energy conversion system. A digital simulation is used in conjunction with the PSO algorithm to...