A learning network model based on fuzzy logic and neural network an d a hybrid learning algorithm BPSOM combining self-organizing learning with BP l earning are proposedis in this paper.

To set up the government of learning type, we need to build up new learning concept, construct the new government organizational structure, perfect the government learning network, constitute the Act of Government Learning Promotion, establish the learning mechanism of the government of learning type, and create organizational culture.

A learning network model based on fuzzy logic and neural network an d a hybrid learning algorithm BPSOM combining self-organizing learning with BP l earning are proposedis in this paper.

This paper lists some representative models of NIDS and algorithms which judge if the actions are intrusions and to what degree they adapt to PPDR model, then puts forward a new NIDS theory model-Self Learning network Intrusion Detection System MODEL (SL_NIDS).

本文首先介绍了NIDS的产生背景，接着介绍了其中比较有影响的模型和检测算法以及现有NIDS存在的问题，在其基础上提出了一种新的网络入侵检测系统理论模型(Self Learning Network Intrusion Detection System MODEL简称SL_NIDS)。

Setting-up of network learning environment is the need for construction of IT in education, digitized learning, network learning and distance education.

A design scheme is put forward on Web engineering graphics and learning network system according to current problems in the course of teaching and learning engineering graphics.

Based on the development of a leadership educational program in the northwestern part of Norway, and by using Actor-Network theory, we conclude that an operational learning network needs to be integrated with concrete business interests.

Using formative evaluation, the results of the project will be highlighted and arguments for a regional learning network will be drawn.

Although the cost has been eliminated, the task of learning network programming has been introduced.

Findings reported for students in cooperative learning settings were also found for teachers who participated in Learning Circles on the Learning Network.

This paper discusses electronic Learning Circles on the AT>amp;amp;T Learning Network1 in the light of cooperative learning theory.

The paper discribes the basic features of the artificial neural network computer-the sixth generation and its applications of image processing, pattern recognition in remote sensing.At first, it compares the major features of artificial neural network computer (NNC) with traditional digital computer. That was parallel massively (distributed) processing; mu-merous processing/memory elements; several mapping structures; learning networks based on train; self-oranigation etc.Then it discribes...

The paper discribes the basic features of the artificial neural network computer-the sixth generation and its applications of image processing, pattern recognition in remote sensing.At first, it compares the major features of artificial neural network computer (NNC) with traditional digital computer. That was parallel massively (distributed) processing; mu-merous processing/memory elements; several mapping structures; learning networks based on train; self-oranigation etc.Then it discribes the princeples of operation of the basic neural unit and some typical' current artificial neural networks and its basic schematic model, which included input, hidden and output layer with connected weights.At final, some approached examples show the special purpose application of image pattern recognition and classification using artificial neural network in the remote sensing field. It can provide some intereting hints.

This paper develops a novel backpropagation networks based adaptive multistep prediction technique for a class of nonlinear dynamical systems, and the prediction mechanism is analyzed. Two isomorphic backpropagation nets are used together to implement the proposed predictor. One is called learning network (LN) while the other is called prediction network (PN). The prediction length and the inputs to the two nets are controlled by settings of some random access memory (RAM) units. Simulation results...

This paper develops a novel backpropagation networks based adaptive multistep prediction technique for a class of nonlinear dynamical systems, and the prediction mechanism is analyzed. Two isomorphic backpropagation nets are used together to implement the proposed predictor. One is called learning network (LN) while the other is called prediction network (PN). The prediction length and the inputs to the two nets are controlled by settings of some random access memory (RAM) units. Simulation results show that this neural nets based adaptive predictor is applicable for a class of nonlinear systems with constant parameters, which may involve a large time delay and stochastic disturbances, so that it provides a new way to deal with a class of nonlinear prediction problems.

This paper develops a novel multilayer neural networks based on adaptive prediction technique for a class of nonlinear dynamical systems, and the prediction mechanism is analyzed . Two isomorphic multilayer nets are used together to implement the proposed predictor. One is called learning network while the other is called prediction network. Simulation results show that this neural nets based on adaptive predictor is applicable for a class of nonlinear systems with constant parameters, which may...

This paper develops a novel multilayer neural networks based on adaptive prediction technique for a class of nonlinear dynamical systems, and the prediction mechanism is analyzed . Two isomorphic multilayer nets are used together to implement the proposed predictor. One is called learning network while the other is called prediction network. Simulation results show that this neural nets based on adaptive predictor is applicable for a class of nonlinear systems with constant parameters, which may involve a large time delay and stochastic disturbances, so that it provides a new way to deal with nonlinear prediction problems.