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A hip protector system using an airbag for prevention of femoral neck fractures is under developing by our group. In the system, instance detection of falling motions by using an appropriate on-line algorithm based on sensor signals is required. The purpose of the present paper is to propose online distinction procedures for human falling motions based on the machine learning, such as the support...
This article integrated reinforcement learning with fuzzy logic method for AUV local planning under the strong sea flow field. A fuzzy behavior is defined to resist the sea flow by giving a extra angle towards sea flow. And Q-learning is used to adjust the peak point of fuzzy membership function of the resisting sea flow behavior. This behavior is complemented by two other behaviors, the moving-to-goal...
This paper describes several new online model-free reinforcement learning (RL) algorithms. We designed three new reinforcement algorithms, namely: QV2, QVMAX, and QVMAX2, that are all based on the QV-learning algorithm, but in contrary to QV-learning, QVMAX and QVMAX2 are off-policy RL algorithms and QV2 is a new on-policy RL algorithm. We experimentally compare these algorithms to a large number...
Multi-robot reinforcement learning is a very challenging area due to several issues, such as large state spaces, difficulty in reward assignment, nondeterministic action selections, and difficulty in merging learned experiences from other robots. In this paper, we propose a dynamic correlation matrix based multi-Q learning (DCM-MultiQ) method for a distributed multi-robot system. A novel dynamic correlation...
Increasing dependence on car-based travel has led to the daily occurrence of freeway congestions around the world. In order to improve the worse and worse traffic congestion situation and solve the problems brought with it, a new kind of effective, fast, and robust method should be presented. Ramp metering has been developed as a traffic management strategy to alleviate congestion on freeways. But,...
In this paper we address a new type of elementary neurofuzzy unit called nullneuron. A nullneuron is a generalization of and/or neurons based on the concept of nullnorm, a category of fuzzy sets operators that generalizes triangular norms and conorms. The nullneuron model is parametrized by an element u, called the absorbing element. If the absorbing element u = 0, then the nullneuron becomes a and...
This paper discussed the sparsed feed-forward neural network, namely, how to determine and delete the redundant neurons and connections in the network. To begin with, the author gives the mathematical definition of feed-forward neural network, and then introduces the partial and topological order to the sparsed algorithm and the learning algorithm of the feed-forward neural network. As a result, the...
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