Fuzzy relational models have been widely investigated and found to be an efficient tool for the identification of complex systems. However, little attention has been given to the linguistic interpretation of these models. The use of relational models is recommended since their development follows a natural sequence based on the original ideas about fuzzy sets and fuzzy logic, involving the esti...

The advantage of solving the complex nonlinear problems by utilizing fuzzy logic methodologies is that the experience or expert’s knowledge described as a fuzzy rule base can be directly embedded into the systems for dealing with the problems. The current limitation of appropriate and automated designing of fuzzy controllers are focused in this paper. The structure discovery and parameter adjus...

An improved universal parallel recurrent neural network canonical architecture, named Recurrent Trainable Neural Network (RTNN), suited for state-space systems identification, and an improved dynamic back-propagation method of its learning, are proposed. The proposed RTNN is studied with various representative examples and the results of its learning are compared with other results,, given in t...

In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of nonlinear system identification, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, securing low-level and high-level int...

The identification of fuzzy c-regression models (FCRM) suffers from several problems characteristic of all calculusbased optimization methods, including good initialization, avoiding local minima and determining the number of clusters. This paper presents a grey-box approach that can solve the above-mentioned problems with the use of prior knowledge based constrained prototypes. The proposed ap...

The classification process support algorithms of shooting hyperspectral data, realizing objects’ identification of the Earth’s surface by means of their hyperspectral features’ analysis, received from the processed space images with application of various similarity measures, are considered. Identification algorithms on the base of Euclidean distance similarity measure, angular similarity measu...

Reinforcement Learning (RL) is thought to be an appropriate paradigm to acquire policies for autonomous learning agents that work without initial knowledge because RL evaluates learning from simple “evaluative” or “critic” information instead of “instructive” information used in Supervised Learning. There are two well-known types of RL, namely Actor-Critic Learning and Q-Leaning. Among them, Q-...

An improved universal parallel recurrent neural network canonical architecture, named Recurrent Trainable Neural Network (RTNN), suited for state-space systems identification, and an improved dynamic back-propagation method of its leaming, are proposed. The proposed R T " is studied with various representative examples and the results of its learning are compared with other results,, given in t...

-This paper presents the application to the identification of coherent generators in a power system based on the fuzzy c-means clustering. In view of the conceptual appropriateness and computational simplicity, the fuzzy c-means give a fast and flexible method for clustering analysis. At first, the coherency measures are derived from the time-domain responses of generators to reveal the relatio...