This paper illustrates a Comparative study of highly non-linear, complex and multivariable Inverted Pendulum (IP) system on Cart using different soft computing techniques. Firstly, a Fuzzy logic controller was designed using triangular and trapezoidal shape Membership functions (MF's). The trapezoidal fuzzy controller shows better results in comparison to triangular fuzzy controller. Secondly, ...

Inherent process and measurement uncertainty has posed a challenging issue on soft sensor development of batch bioprocesses. In this paper, a new soft sensor modeling framework is proposed by integrating Bayesian inference strategy with two-stage support vector regression (SVR) method. The Bayesian inference procedure is first designed to identify measurement biases and misalignments via poster...

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) were used to estimate shear stress distribution in streams. The methods were applied to the 145 field data gauged from four different sites on the Sarimsakli and Sosun streams in Turkey. The accuracy of the applied models was compared with the multiple-linear regression (MLR). The results showed t...

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the opt...


 The development of virtual sensors predicting the desired output requires a careful selection input variables for model construction. In an industrial environment, datasets contain many instrumentation system measures; however, these are often non-relevant or excessive information. This paper proposes variable algorithm based on mutual information examination, redundancy analysis, and re...

• PCA is effective in generating voltage controlling areas near critical point. ANFIS and KNN were the best regression algorithms when load predictions. ANN, SVR DT showed to be inferior Voltage Loading mapping. a fast assessment tool for predictions due its rules. The aim of this paper compare system loading mapping capabilities variety algorithms, such as Adaptive Network based Fuzzy Inferenc...

Machine learning (ML)-based models, decision tree and ANFIS, were used to predict the degree of surface checking bending properties 30-month weathered thermally modified timber. The results showed that investigated initial board did not allow accurate predictions checks. ML regression clustering analysis confirmed important variables for dynamic stiffness, acoustic velocity, density lowest loca...

This paper discusses the application of support vector machine in the area of identification of nonlinear dynamical systems. The aim of this paper is to identify suitable model structure for nonlinear dynamic system. In this paper, Adaptive Neuro Fuzzy Inference Systems (ANFIS) and Support Vector Regression (SVR) models are applied for identification of highly nonlinear dynamic process. The res...

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in ord...

In a great diversity of knowledge areas, the variables that are involved in the behavior of a complex system, perform normally, a non-linear system. The search of a function that express those behavior, requires techniques as mathematics optimization techniques or others. The new paradigms introduced in the soft computing, as fuzzy logic, neuronal networks, genetics algorithms and the fusion of...