Many optical flow estimation techniques are based on the differential optical flow equation. These algorithms involve solving over-determined systems of optical flow equations. Least squares (LS) estimation is usually used to solve these systems even though the underlying noise does not conform to the model implied by LS estimation. To ameliorate this problem, work has been done using the total...

Least Squares (LS) estimation is a classical problem, often arising in practice. When the dimension of the problem is large, the solution may be difficult to obtain, due to complexity reasons. A general way to reduce the complexity is that of breaking the problem in smaller sub-problems. Following this approach, in the paper we introduce an Alternating Least Squares (ALS) algorithm that finds t...

 This paper deals with the Least Squares (LS) design of fullband higher order digital differentiators. The contribution extends a previous work on the problem presenting some new results (analytical, graphical and numerical) and conclusions. The design method for even and odd arbitrary k-th order differentiators based on the LS integral error criterion is considered. A few new closedform relat...

This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses ...

Many practical systems such as thermal processes, chemical processes and biological systems, etc., have inherent time-delay. If the time-delay used in the system model for controller design does not coincide with the actual process time-delay, a closed-loop system may be unstable or exhibits unacceptable transient response characteristics. Therefore, the problem of identifying such a system is ...

Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This success is mainly attributed to the power of deep neural networks to learn rich domain representations for approximating the value function or policy. Batch reinforcement learning methods with linear representations, on the...

A new algorithm based on least-squares method for harmonic and interharmonic analysis is proposed. The approach employs least-squares method and is based on a linear prediction relation for multiple sinusoidal signals. It is shown that the method allows for accurate harmonic and interharmonic estimation with high frequency resolution. The approach is compared with that of DFT method and prony m...