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In order to solve the problem of "information overflow" in e-learning, an algorithm based on Hebbian learning law is proposed for constructing self-organized communities which can automatically group e-learners according to their learning interests. Unlike filtering methods,this algorithm takes into consideration of the distributed open environment of e-learning. This paper designed a peer-to-peer...
A framework for a new type of estimation of distribution algorithms (EDAs) is developed. It is similar to the Bayesian optimization algorithm (BOA) except that it replaces Bayesian network model with estimation of schema distribution based on maximum entropy. As structure learning of Bayesian network is not needed, it reduces the computational cost. The experimental results show that the new algorithms...
A hybrid algorithm based on Extremal Optimization (EO) with adaptive levy mutation and Differential Evolution (HEODE) was proposed in this paper. It applied the idea of combination mechanism of global and local search. In the process of the global search, DE is an evolutionary algorithm based on the difference in group that can quickly approach a approximate optimal solution. During the local search,...
Recently in-depth analysis of network security vulnerability must consider attacker exploits not just in isolation, but also in combination. The general approach to this problem is to compute attack graphs using a variety of graph-based algorithms. However, such methods generally suffer the exponential state space problem. Therefore, this paper brings forward two conceptions of vulnerability correlation...
For measuring the uncertainty of behavior, the average rough coverage doesn't consider the difference among middle learning stages in reinforcement learning. To address this problem, a novel measure model based on generalized approximation spaces is proposed. In this study, uncertainty is regarded as the local feature of a state and used to guide future learning. Data-driven Q-learning based this...
The paper aims to present a new scheme of synchronization-based topology identification for a class of weighted general complex dynamical networks with time-varying coupling delays. By combining the adaptive control method and the Razumikhin-type theorem, a new feedback technique to identify the exact topology of the dynamical networks with time-varying coupling delay has been proposed. In comparison...
Network traffic prediction is an important research aspect of network behavior. Conventionally, ARMA time sequence model is usually adopted in network traffic prediction. However, the parameters used in normal time sequence models are difficult to be estimated and the nonstationary time sequence problem can not be processed using ARMA time sequence model. The neural network techniques may memory large...
Sparse coding has high-performance encoding and ability to express images, sparse encoding basis vector plays a crucial role. The computational complexity of the most existing sparse coding basis vectors of is relatively large. In order to reduce the computational complexity and save the time to train basis vectors. A new Hebbian rules based method for computation of sparse coding basis vectors is...
When apply Q-learning to complex real-world problems, the learning process is long enough to make this method unpractical. The major cause is Q-learning requires the agent to visit every state-action transition infinitely often for making Q value convergent. We propose a state-cluster based Q-learning method to accelerate convergence and shorten learning process. This method creates the state-cluster...
In agile manufacture system, the strategic focus of enterprises is shifting from the traditional model in production to a created model due to the changes in the manufacturing environment, more enterprises cut across the boundary of enterprises to develop new products with the joint quantitative enterprisers in agile manufacture . Hegemony enterprise needs a rapid & synthetic qualitative &...
The speech cepstral features are important parameter in automatic speech recognition (ASR), which symbolizes the property of human auditory system (HAS). The mel-frequency cepstral coefficients (MFCC) are the most widely used features in speech recognition field. This paper discusses about the algorithm of chirp Z-transform (CZT), and the CZT-based cepstral coefficients are proposed along with the...
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