Behavior Learning of Autonomous Robots by Modified Learning Vector Quantization
نویسندگان
چکیده
منابع مشابه
Learning to Acquire Autonomous Behavior: Cooperation by Humanoid Robots
In this paper, we describe a cooperative transportation to a target position with two humanoid robots and introduce a machine learning approach to solving the problem. The difficulty of the task lies on the fact that each position shifts with the other’s while they are moving. Therefore, it is necessary to correct the position in a real-time manner. However, it is difficult to generate such an ...
متن کاملGeneralized Learning Vector Quantization
We propose a new learning method, "Generalized Learning Vector Quantization (GLVQ)," in which reference vectors are updated based on the steepest descent method in order to minimize the cost function . The cost function is determined so that the obtained learning rule satisfies the convergence condition. We prove that Kohonen's rule as used in LVQ does not satisfy the convergence condition and ...
متن کاملMutual Learning by Autonomous Mobile Robots
This paper describes a reinforcement learning algorithm for small mobile robots based on sets of fuzzy automata. The task the robots have to learn is how to reactively avoid obstacles. In mutual learning two robots learn simultaneously, with the experiences of one robot being passed to the second robot. We show that the robot that receives the other robots experiences learns more quickly and ro...
متن کاملRobust vector quantization by competitive learning
Competitive neural networks can be used to e ciently quantize image and video data. We discuss a novel class of vector quantizers which perform noise robust data compression. The vector quantizers are trained to simultaneously compensate channel noise and code vector elimination noise. The training algorithm to estimate code vectors is derived by the maximum entropy principle in the spirit of d...
متن کاملCursive character recognition by learning vector quantization
This paper presents a cursive character recognizer embedded in an o-line cursive script recognition system. The recognizer is composed of two modules: the ®rst one is a feature extractor, the second one a learning vector quantizer. The selected feature set was compared to Zernike polynomials using the same classi®er. Experiments are reported on a database of about 49,000 isolated characters.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Transactions of the Society of Instrument and Control Engineers
سال: 2001
ISSN: 0453-4654
DOI: 10.9746/sicetr1965.37.1162