The Proportional Integral Derivative (PID) controller is the most widely used industrial device to monitoring and controlling processes. There are numerous methods for estimating the controller parameters, in general, resolving particular cases. Current trends in parameter estimation minimize an integral performance criterion. Therefore, the calculation of the controller parameters is proposed ...

A new fuzzy bang–bang relay controller (FBBRC) is introduced in this paper. The new controller is inherently optimal due to its bang–bang property. The controller has fuzzy decision-making capability in its inputs and have two fixed levels bang–bang output. Consequently, the tuning of FBBRC is restricted to input parameters only in comparison to standard fuzzy logic controller (FLC) where the o...

Online personalization of hearing instruments refers to learning preferred tuning parameter values from user feedback through a control wheel (or remote control), during normal operation of the hearing aid. We perform hearing aid parameter steering by applying a linear map from acoustic features to tuning parameters. We formulate personalization of the steering parameters as the maximization of...

We propose a class of multiplicative algorithms for Nonnegative Matrix Factorization (NMF) which are robust with respect to noise and outliers. To achieve this, we formulate a new family generalized divergences referred to as the Alpha-Beta-divergences (AB-divergences), which are parameterized by the two tuning parameters, alpha and beta, and smoothly connect the fundamental Alpha-, Betaand Gam...

In chemical industries in mid 90’s, process engineers were using the classical controller tuning methods to find the controller parameter values like Kp, Ki ,and Kd. The classical controller tuning methods may require complex computations to identify the controller parameters. With the evolution of soft computing methods these classical control methods were eliminated. This paper focus on desig...

In this paper, we proposed a self-adaptive lasso method for variable selection in regression problems. Unlike the popular lasso method, the proposed method introduces a specific tuning parameter for each regression coefficient. We modeled self-adaptive lasso in a Bayesian framework and developed an efficient Gibbs sampling algorithm to automatically select these tuning parameters and estimate t...

We previously developed building blocks to enable end-users to construct customized sensor-based embedded systems to help monitor and control a users' environment. Because design objectives, like battery lifetime, reliability, and responsiveness, vary across applications, these building blocks have software-configurable parameters that control features like operating voltage, frequency, and com...

While inlining is one of the heavily used optimizations for compiling Java programs, its overly aggressive usage can actually hurt the program performance. Therefore, the inlining heuristics used in various Java virtual machines need to be appropriately tuned in order to maximize inlining benefits without introducing its potential cost. However, the current tuning process is usually performed m...

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Usin...