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...

A decision list is an ordered list of conjunctive rules (Rivest 1987). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem -the classification accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in ...

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Machine Learning is often challenged by insufficient labeled data. Previous methods employing implicit commonsense knowledge of pre-trained language models (PLMs) or pattern-based symbolic have achieved great success in mitigating manual annotation efforts. In this paper, we focus on the collaboration among different sources and present KICE, a Knowledge-evolving framework Iterative Consolidati...

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...