The author presents a learning algorithm and capabilities of perceptron-like neural networks whose outputs and inputs are directly connected to plants just like ordinary feedback controllers. This simple configuration includes the difficulty of teaching the network. In addition, it is preferable to let the network learn so that a global and arbitrary evaluation of the total responses of the plant will be optimized eventually. In order to satisfy these needs, genetic algorithms are modified to accommodate the network learning procedure. This procedure is a kind of simulated evolution process in which a group of networks gradually improves as a whole, by crossing over connection weights among them, or by mutational changes of the weights, according to fitness values assigned to each network by a global evaluation. Simulations demonstrate that these networks can be optimized in terms of various evaluations, and they can discover schemes by themselves, such as state estimation and nonlinear control.
The goo1 of this study was to investigate communication with an intelligent machine such as a human-friendly robot in an environment where human and robot coerist. The face and its expressions are crucial for communication, so we have developed a face robot which has a human-like face and can express facial expre~sions similar to a human being.We used air cylinders with pressurized air /or the Mark I face robot which was 1.5 rimes bigger than human face. In order to realize human face size, we then decided to use elecfricol
shape memory alloy (3M) acluators to produce facial expressions (Mark Io. We realized the. human size f m e robot though, SMA did not have enough dwabiliity and power for expressing facial expressions. For Mark .III we then selected McKibben-rype pneumatic actuator to display/icial .&presions.
In this paper we show the history of the face robot we have developed, discuss how to build the face robot by using McKibben-ppe pneumatic actuator, and show the basic abilityfor qressing focial expressions.
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