![]() It involves not only the brain cells and their connections but also certain factors such as attention, memory, motivation and stress. ![]() It can be seen than at its basic functional level, learning is without a doubt a complex activity. The more dendrite connections there are, the more learning there would be. Each neuron is formed by a body or soma, and axon that sends information and thousands of dendrites for receiving data. Neurons in humans are useless individually but extremely useful and powerful when working together. Learning in humans involves approximately 100 billion of neurons (brain cells). In this application the algorithm created affordable grasping postures from limited examples, creates its own knowledge and stores data in memory data in memory in order to recognise whether it has been on a similar situation. Finally, the first application where CODA is used, a humanoid hand is presented. It can be seen graphically how new data that has a completely new probability distribution. The clonal/mutation results are presented with a simple example. The algorithm is explained with pseudocode, flowcharts and block diagrams. CODA algorithm is presented and explained in detail in order to understand how these three principles are used. It also uses the self-organised memory in order to reduce searching time in the whole action-state space by searching in specific clusters. The algorithm imitates from the Natural Immune System the clonal procedure for obtaining a repertoire of antibodies from a single antigen. ![]() Is it can learn from limited data samples- that is given a single example and the algorithm will create its own knowledge. The main advantage of this algorithm named CODA (Cognition from Data). This document presents the design of an algorithm that takes on its basis: reinforcement learning, learning from demonstration and most importantly Artificial Immune Systems.
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