[PDF] Top 20 GA-based reinforcement learning for neural networks
Has 10000 "GA-based reinforcement learning for neural networks" found on our website. Below are the top 20 most common "GA-based reinforcement learning for neural networks".
GA-based reinforcement learning for neural networks
... suitability for any purpose of the ...liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or ... See full document
16
Controlling chaos by GA-based reinforcement learning neural network
... and GA into the actor-critic architecture to form a new reinforcement learning system, called the TDGAR learning ...TDGAR learning system, we can train a neural controller ... See full document
14
GA-based fuzzy reinforcement learning for control of a magnetic bearing system
... 1) Learning Algorithm for the Critic Network: When both the reinforcement signal and input patterns from the environ- ment depend arbitrarily on the past history of the action net- work outputs and ... See full document
14
REINFORCEMENT STRUCTURE PARAMETER LEARNING FOR NEURAL-NETWORK-BASED FUZZY-LOGIC CONTROL-SYSTEMS
... After the consequents of rule nodes are determined for both the action and evaluation networks (i.e., when the structure leaming process is done and the structure will not be [r] ... See full document
18
Reinforcement learning for an ART-based fuzzy adaptive learning control network
... IEEE TRANSACTIONS ON NEURAL. 3, MAY 1996 reinforcement signals from the critic network, which has only one output node since it is used to predict the external scalar reinforcem[r] ... See full document
23
Genetic-based reinforcement learning for fuzzy logic control systems
... The proposed GR-FLCS is constructed by integrating a real-coded genetic algorithm with a time accumulator as the fitness evaluator, a success criterion, a fuzzy logic controller[r] ... See full document
4
Reinforcement hybrid evolutionary learning for recurrent wavelet-based neurofuzzy systems
... and ; then . The four input variables and the output are normalized between zero and one over the following ranges: . The values are floating-point numbers initially assigned using the R-HELA. In the proposed R-HELA ... See full document
17
Neural Networks and Learning Machines
... choice for the initial values of the synaptic weights and thresholds of the network can be of tremendous help in a successful network ...the learning process to slow ...positive. For these rea- sons, ... See full document
937
GENERALIZED PERCEPTRON LEARNING RULE AND ITS IMPLICATIONS FOR PHOTOREFRACTIVE NEURAL NETWORKS
... The range of the gain factor () for perceptron convergence is useful in hardware imple- mentation, where the interconnection weight may suffer gain or loss during t[r] ... See full document
6
Machine learning with parallel neural networks for analyzing and forecasting electricity demand
... well-calibrated for their robustness when dealing with the dis- ruptive elements ...weather). Based on the application of big data driven analytics, we propose a novel machine learning method ... See full document
29
Hand-Eye Coordination for Visual Targets by Hebbian Learning Using Neural Networks
... stage neural network without histogram equalization With the implementation of histogram equalization in neuron allocation, the performances of both joints are improved by ... See full document
17
Reinforcement learning for an ART-based fuzzy adaptive learning control network (vol 7, pg 709, 1996)
... ART-Based Fuzzy Adaptive Learning Control Network" Cheng-Jian Lin and Chin-Teng Lin.. qtemal '.[r] ... See full document
1
Unsupervised query-based learning of neural networks using selective-attention and self-regulation
... positive for the system, the distinction between want (self-focus from internal desire) and need (environment- focus from external stimulus) is ...used for neural-network ... See full document
13
Reinforcement learning and robust control for robot compliance tasks
... even for compliance tasks of the same ...a reinforcement learning and robust control scheme for robot compliance ...A reinforcement learning mechanism is used to tackle ... See full document
18
Scalable Power Management Using Multilevel Reinforcement Learning for Multiprocessors
... Algorithms based on the multilevel paradigm are able to produce high-quality solu- tions in a small amount of time, while flat algorithms face lots of small elements thus lack of global views over the ... See full document
23
Genetic reinforcement learning through symbiotic evolution for fuzzy controller design
... supervised learning algorithms that require exact supervised input-output training ...genetic reinforcement learning algorithm, called the SEFC design method, is proposed for designing fuzzy ... See full document
13
Precise image alignment using cooperative neural-fuzzy networks with association rule mining-based evolutionary learning algorithm
... area- based methods, such as correlation-like methods, they are popular for real-time applications because of simplicity and hardware ...table for complicated geometric ... See full document
16
Power-spectrum-based neural-net connection admission control for multimedia networks
... parameters for PNCAC are both unified metrics corre- sponding to traffic characteristics of all different sources and possess the additive property, while the connection number [r] ... See full document
7
Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks
... As for the connections scheduled by the deadline driven algorithm, although the classification rate of the GANFDT can be very high after the training process, we still cannot ensure it will not misjudge any ... See full document
14
Applying Reinforcement Learning for Game AI in a Tank-Battle Game
... necessary for the NPCs (tanks) in the game. The reinforcement learning method is used as the AI to be added to the original ...held based on two different experiment ...the ... See full document
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