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This study aimed to improve the precision of multiple robots' self-localization in the standard platform league of RoboCup, i.e. a robotic soccer competition. For improving the precision of the self-localization, we proposed a new technique that uses an external camera out of the field for assistance. Robots in the field use the unscented particle filter that estimates their position from some landmarks...
The authors developed an image processing technique for estimating the progress of eating behavior of an elderly recipient. The image processing is for depth images and does not require any visual markers on plateware such as dishes. The authors' medication management support system can make a reminder at the correct time (e.g., after eating) depending on the estimated progress. In addition, a human...
Recently, researches for the intelligent robots incorporating knowledge of neuroscience have been actively carried out. In particular, a lot of researchers making use of reinforcement learning have been seen, especially, “Reinforcement learning methods with emotions”, that has already proposed so far, is very attractive method because it made us possible to achieve the complicated object, which could...
Time series data analyze and prediction is very important to the study of nonlinear phenomenon. Studies of time series prediction have a long history since last century, linear models such as autoregressive integrated moving average (ARIMA) model, and nonlinear models such as multi-layer perceptron (MLP) are well-known. As the state-of-art method, a deep belief net (DBN) using multiple Restricted...
Electroencephalogram (EEG) signals are widely used in brain-computer interface (BCI) recently. By classifying the signals, mental tasks in the brain are available to be estimated and then the results can be used in the communication between human and external devices or robots. Many classifiers, such as support vector machine (SVM), multi-layer perceptron (MLP), and self-organizing map (SOM), etc...
Deep belief nets (DBNs) with multiple artificial neural networks (ANNs) have attracted many researchers recently. In this paper, we propose to compose restricted Boltzmann machine (RBM) and multi-layer perceptron (MLP) as a DBN to predict chaotic time series data, such as the Lorenz chaos and the Henon map. Experiment results showed that in the sense of prediction precision, the novel DBN performed...
An asymmetric neighborhood function was proposed by Aoki and Aoyagi to instead of symmetric neighborhood function in conventional Kohonen's self-organizing map (SOM) to avoid topological twist of the order of units during training process. Meanwhile, a one dimensional ring type growing SOM was proposed by Ohta and Saito to reduce the unnecessary increasing of units of conventional 2-D growing SOM...
Wavelet neural network (WNN) has high function approximation capability, because it consists of neurons, each of which has a localized and vibratory waveform, and the center of the waveform and its scaling and spatial extent/reduction are adjustable. Therefore it has outstanding ability to adapt to changes of environments. In the field of control engineering, Neural Network (NN) and Fuzzy Neural Network...
In this paper, we propose an effective method to configure a dynamical structure of each agent constituting Multi-Agent System (MAS) on a decentralized adaptive control. It is important that each agent does decision-making while configuring its own desirable dynamical characteristics and adapting to environmental changes. In conventional researches, the dynamics of each agent is modeled by neural...
As a dynamic auto-associative memory model, Aihara et al. has proposed a chaotic neural network (CNN) which is consisted by interconnected chaotic neurons is able to recollect stored patterns dynamically. To realize mutual association of plural time series patterns, Kuremoto et al. proposed to combine multiple CNN layers as a MCNN and applied it to a mathematical hippocampus model. However, recollection...
When human does a decision-making, h/she finally does it using the various functions in the brain. He/she also has the ability to learn to improve the decision and get better results than before. Reinforcement learning, one of machine learning methods, is mimicking of learning function of the biological brain's basal ganglia. In this study, we propose a novel method that combines the conventional...
This paper proposes a voice command learning system for partner robots acquiring communication ability with instructors. Parameter-less Growing Self-Organizing Map (PL-G-SOM), an intelligent pattern recognition model given by our previous work, is used and computational feeling of robots is also adopted to improve the human-machine interaction system. AIBO robot was used in the experiment and the...
Robust control theory generally guarantees robustness and stability of the closed-loop system, however it requires mathematical model of the system to design the control system. Therefore, it can't often deal with nonlinear systems because of difficulty of modeling of the system. Other, reinforcement learning method can deal with the nonlinear system without mathematical model, however, it usually...
This paper proposes the intelligent tracking control of the target by group of agents with nonlinear dynamics. In the proposed method, agents can exchange only information of positions and the group of agents track the target based on only its position, taking a predefined formation. In the real world, the method that agents do not require a lot of information is useful for the case of existing communication...
A hybrid intelligent control system model which combines high-level time Petri net (HLTPN) and Reinforcement Learning (RL) is proposed. In this model, the control system is modeled by HLTPN and system state last time is presented as transitions delay time. For optimizing the transition delay time through learning, a value item is appended to delay time of transition for recording the reward from environment...
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