In this paper, the observer-based iterative learning control with/without evolutionary programming algorithm is proposed for MIMO nonlinear systems. While the learning gain involves some unmeasurable states, this paper proposes the observer-based iterative learning control (ILC) for nonlinear systems and guarantees the tracking error convergences to zero via continual learning. Moreover, a suff...

This report is a complement to the working document [4], where a sparse associative network is described. This report shows that the net learning rule in [4] can be viewed as the solution to a weighted least squares problem. This means that we can apply the theory framework of least squares problems, and compare the net rule with some other iterative algorithms that solve the same problem. The ...

We describe an approach for iterative information fusion using a context-dependent Reasoner called Pequliar. The system basically consists of a query processor with fusion capability and a Reasoner with learning capability. The query processor first performs a query to produce some initial results. If the initial results are uninformative, then the Reasoner guided by the user creates a more ela...

In this paper, the application of Iterative Learning Control (ILC) algorithm in two-dimensional problems is discussed. Furthermore, this method of control is combined with optimal control to produce a powerful method of control for 2-D systems. For this issue by using 1-D model of WAM (Wave Advanced Model), the 2-D model is converted to 1-D model. To generate the control rule in the every itera...

In this paper, we develop a curved search algorithm which uses second-order information, for the learning algorithm for a supervised neural network. With the objective of reducing the training time, we introduce a fuzzy controller for adjusting the first and second-order approximation parameters in the iterative method to further reduce the training time and to avoid the spikes in the learning ...

This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics-based Takagi–Sugeno–Kang ~TSK! rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two-stage evolutionary algorithm based on MOGUL ~a methodology to obtain Genetic Fuz...

nowadays, it has demonstrated that viruses can be transmitted by water and foods. therefore, it causes the research to develop for detecting different viruses in water and foods. among foods, milk can transfer potentially pathogenic viruses. on the other hand, to achieve every method for recovery and extraction of viruses in raw milk it needs to know about impact of milk components on viruses. ...

In 1965 Lofti A. Zadeh proposed fuzzy sets as a generalization of crisp (or classic) sets to address the incapability of crisp sets to model uncertainty and vagueness inherent in the real world. Initially, fuzzy sets did not receive a very warm welcome as many academics stood skeptical towards a theory of “imprecise” mathematics. In the middle to late 1980’s the success of fuzzy controllers bro...

An automatic threshold selection method is proposed for biomedical image analysis based on a histogram coding scheme. We show that the threshold values can be determined based on the well-known Lloyd-Max scalar quantization rule, which is optimal in the sense of achieving minimum mean square error distortion. We derive an iterative self-organizing learning rule for determining the threshold lev...

The paper presents an evolutionary approach for generating fuzzy rule based classifier. First, a classification problem is divided into several two-class problems following a fuzzy unordered class binarization scheme; next, a fuzzy rule is evolved (not only the condition but the fuzzy sets are evolved (tuned) too) for each two-class problem using a Michigan iterative learning approach; finally,...