Realistic systems generally are systems with various inputs and outputs also known as Multiple Input Multiple Output (MIMO). Such systems usually prove to be complex and difficult to model and control purposes. Therefore, decomposition was used to separate individual inputs and outputs. A PID is assigned to each individual pair to regulate desired settling time. Suitable parameters of PIDs obta...

This paper presents the adaptation of a single layer complex valued neural network (NN) to use entropy in the cost function instead of the usual mean squared error (MSE). This network has the good property of having only one layer so that there is no need to search for the number of hidden layer neurons: the topology is completely determined by the problem. We extend the existing stochastic MSE...

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 Error Whitening Criterion (EWC) to tackle the problem of linear system identification in the presence of additive white disturbances. We will motivate the theory behind ...

The threshold region mean squared error (MSE) performance of the Capon-MVDR algorithm is predicted via an adaptation of an interval error based method referred to herein as the method of interval errors (MIE). MIE requires good approximations of two quantities: (i) interval error probabilities, and (ii) the algorithm asymptotic (SNR→ ∞) MSE performance. Exact pairwise error probabilities for th...

Predicted electricity consumption is needed to perform energy management. Electricity prediction also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) predict electrical loads results compared measurable loads. Laboratory have their own characteristics when residential, commercial, or indus...

This paper presents three regression models that predict the lithium-ion battery life for electric cars based on a supervised machine learning algorithm. The linear regression, bagging regressor, and random forest regressor will be compared capacity prediction of batteries voltage-dependent per-cell modeling. When sufficient test data are available, algorithms train this model to give promising...

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation demand Turkey’s mainland with use mixed MNN, WAO, SVM. Imports, exports, gross domestic product (GDP), population data based on input from 1980 2019 Turkey, demand...

introductapplication of neural network of multi layers perceptron (mlp) in site selection of municipal solid ‎waste landfilling with emphasis on hydrogeomorphic characteristics (case study: fereydoonshahr city)‎introduction‏:‏cities are at the nexus of a further threat to the environment, namely the production of an increasing ‎quantity and complexity of wastes. the estimated quantity of munici...