1. There are no standards for learning algorithms or for training networks.

2. An unsupervised learning algorithm might emphasize cooperation among clusters of processing elements.

3. Weights, summing and transfer functions, and learning algorithms all rely heavily on mathematics.

4. A learning algorithm of procedure neural networks is proposed.

5. An average reward reinforcement learning algorithm for control Markov chains is presented.

6. Immune algorithm is a kind of intelligent learning algorithm which simulates the biology immunity systems.

6. try its best to gather and create good sentences.

7. This paper presents a learning algorithm based on artificial immune which has the characteristics of recognition variable and self-adjustment.

8. In the application studies of ADP methods, a learning algorithm solving the queueing system optimization problem and a direct adaptive optimal control method are suggested.

9. The rank learning algorithm based on the decision tree is able to process categorical data and select relative features.

10. In this paper, a hierarchical reinforcement learning algorithm is investigated for Markov Decision Process with average reward.

11. In this paper, a hierarchical reinforcement learning algorithm is investigated for Markov decision process with average cost.

12. As for it, by improving learning algorithm of traditional RBF neural network, a new dynamic cluster-based self-generated method for hidden layer nodes is proposed.

13. Furthermore, the exploring study of the reinforcement learning algorithm adopted in the MA collaborative decision was offered.

14. We then describe an entropy penalized AMS learning algorithm on Gaussian mixture.

15. How the interconnection weights are changed is the function of the learning algorithm.

16. Their goal is to advance both hardware implementation and learning algorithms.

17. To the problems higher rate of false retrieval in anomaly detection system due to the uncertainty of intrusion, this paper presents an Anomaly Detection Model Based on Q- Learning Algorithm (QLADM).

18. Based on the above - mentioned result a fast pseudo - inverse matrix learning algorithm is obtained for the CNN.

19. In this paper, we proposed a parallel BP neural network learning algorithm with the support of PC cluster under the circumstance of PVM(Parallel Virtual Machine).

20. The Counterpropagation Network(CPN) can be applied to image compression as a vector quantizer. However, the CPN learning algorithm has two obvious disadvantages in codebook designing.

21. A fire signal detection model based on fuzzy neural - network and its learning algorithm are this paper.

22. For the above reasons, the extended Kalman filter is proposed as RBF learning algorithm, and the biradial function is used in hidden layer.

23. In this paper we proposed an adaptive equalizer based on complex Hebbian-type learning algorithm.

24. Derived from the standpoint of minimum error parameter estimation, a fast learning algorithm for solving interconnection matrix W of the network is of fast convergence property.

25. To solve the problem of slow computation speed and low image matching accuracy, a new approach to image matching using population-based increased learning algorithm (PBIL) was proposed.

26. Then, we proved that the same condition is sufficient for the robustness of the proposed learning algorithm against state disturbance, output measurement noise, and initialization error.

27. The paper first presents an objective model of task scheduling, and then based on the analysis of Q learning algorithm, the Markov decision process description of the scheduling problem is given.

28. The experiments on Maze problem show that COSTRLA has better performance than the classical reinforcement learning algorithm for solving goal state problem.

29. This tracker uses a modified radial basis function (RBF) neural network which incorporates a learning algorithm for tracking the noisy chaotic signal under parameter variation.

30. Swarm-robot simulation system is a digital system that is used for the demonstration of the control architecture, cooperative control, learning algorithm of swarm-robot system.

31. Fourthly . making use of Learning from examples based on information theory, machine learning algorithm and machine learning decision tree is realized.

32. And through compare with non-Evolutionary Map building method, the Evolutionary Reinforcement learning algorithm can increase search map efficiency and expedite convergence speed of global map.

33. In this paper, we develop a kernel based reinforcement learning algorithm, which solve the problems with continuous state spaces directly.

34. A kind of online learning algorithm of Neural Network Decoupler based on MIMO cross-correlation function was proposed on the frame of distributed decoupling system .

35. A fighter safe landing lateral-directional control method is presented based on reinforcement learning algorithm(RL), using hierarchical control of dynamical large-scale systems theory.

36. A hybrid GA-BP learning algorithm is applied to a real condensation process. The computer aided optimum design is realized.

37. Then , a Phases Anomaly Detection Pattern Learning algorithm, PADPL, was put forward.

38. The simulation shows that using the fuzzy model based reinforcement functions in reinforcement learning algorithm can further speed up the convergence than using model-free reinforcement functions.

39. The reservoir density prediction technique is a non-linear seismic prediction approach based on improved hybrid intelligent learning algorithm.

40. The topologic structure and learning algorithm of the rough neural network are given, and the approximation theorem of the rough neural network is presented.

41. The general similarity-based approximate reasoning method (GSBAR) needs a learning algorithm to find its functional parameters based on various cases.

42. Based on simulated annealing and reinforcement learning algorithm, a hybrid intelligent controller was proposed to ship steering.

43. In this paper Q reinforcement learning algorithm is adopted for mobile robot local path planning. It makes mobile robot resolve the problem of local path planning in a complex environment.

44. Observational Learning Algorithm ( OLA ) is an ensemble method based on social learning theory.

45. Q learning algorithm is the most popular reinforcement learning algorithm, but the algorithm exist some problems.

46. A reinforcement learning algorithm based on process reward and prioritized sweeping is presented as interference solving strategy.

47. A learning algorithm is presented to acquire and optimize rules part of speech tagging.

48. Using this feature, the structure and learning algorithm of hierarchical functional network were used to obtain numerical approximation in the multiple numerical integral .

49. The paper presents an unsupervised learning algorithm to learn answer pattern for answer extraction module of Chinese Question Answering(QA).

50. The realization method and learning algorithm for reinforcement learning system with continuous actions was presented.

50.

51. A new algorithm is presented for training of multilayer feed forward networks by integrating a genetic algorithm with an adaptive conjugate gradient neural network learning algorithm.

52. Multi-Agent technology achieves the personalized in ITS, and reinforcement learning algorithm makes teaching strategies with the intelligent.

53. Applies feed forward neural network to the N QUEENs question, and sdves the local minimization question using a rapid learning algorithm with variable learning factors. Gives several research results.

54. This paper proposes CF - WFCM algorithm including feature weight learning algorithm and clustering algorithm.

55. In this paper, by analyzing the functional network, a new model and learning algorithm of the serial functional networks is proposed.

56. This paper presents a color images region growing method based on online learning algorithm, which is used for content-based image retrieval systems.