This paper proposes a new fuzzy neural network based reinforcement adaptive iterative learning controller for a class of nonlinear systems. Different from some existing reinforcement learning schemes, the reinforcement adaptive iterative learning controller has the advantages of rigorous proofs without using an approximation of the plant Jacobian. The critic is appended into the reinforcement a...

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Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies. Usually big data tools perform computation in batch-mode and are not optimized for iterative pr...

Successful image reconstruction requires the recognition of a scene and the generation of a clean image of that scene. We propose to use recurrent neural networks for both analysis and synthesis. The networks have a hierarchical architecture that represents images in multiple scales with different degrees of abstraction. The mapping between these representations is mediated by a local connectio...

This paper provides a systematic study of incremental learning from noise-free and from noisy data, thereby distinguishing between learning from only positive data and from both positive and negative data. Our study relies on the notion of noisy data introduced in 22]. The basic scenario, named iterative learning, is as follows. In every learning stage, an algorithmic learner takes as input one...

A method of incorporating experience in iterative learning controllers is proposed in this paper. It is proposed that if the previous experience of the controller can be used in the selection of the initial control input for a new desired trajectory tracking task, the convergence of the iterative learning controller can be improved without modifying the structure of the controllers. Therefore t...

Semi-supervised is the machine learning field. In the previous work, selection of pairwise constraints for semi-supervised clustering is resolved using active learning method in an iterative manner. Semi-supervised clustering derived from the pairwise constraints. The pairwise constraint depends on the two kinds of constraints such as must-link and cannot-link.In this system, enhanced iterative...

Semi-supervised is the machine learning field. In the previous work, selection of pairwise constraints for semi-supervised clustering is resolved using active learning method in an iterative manner. Semi-supervised clustering derived from the pairwise constraints. The pairwise constraint depends on the two kinds of constraints such as must-link and cannot-link.In this system, enhanced iterative...