38928933
Two Adaptive Matching Learning Algorithms for Independent Component Analysis
Independent component analysis (ICA) has been applied in many fields of signal processing and many ICA learning algorithms have been proposed from different perspectives. However, there is still a lack of a deep mathematical theory to describe the ICA learning algorithm or problem, especially in the cases of both super- and sub-Gaussian sources. In this paper, from the point of view of the one-bit-matching principle, we propose two adaptive matching learning algorithms for the general ICA problem. It is shown by the simulation experiments that the adaptive matching learning algorithms can efficiently solve the ICA problem with both super- and sub-Gaussian sources and outperform the typical existing ICA algorithms in certain aspects.
361698
The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm
The rival penalized competitive learning (RPCL) algorithm has been developed to make the clustering analysis on a set of sample data in which the number of clusters is unknown, and recent theoretical analysis shows that it can be constructed by minimizing a special kind of cost function on the sample data. In this paper, we use the Mahalanobis distance instead of the Euclidean distance in the cost function computation and propose the Mahalanobis distance based rival penalized competitive learning (MDRPCL) algorithm. It is demonstrated by the experiments that the MDRPCL algorithm can be successful to determine the number of elliptical clusters in a data set and lead to a good classification result.
370694
Further Results on the Asymptotic Memory Capacity of the Generalized Hopfield Network
This paper presents a further theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network (GHN) under the perceptron learning scheme. It has been proved that the asymptotic memory capacity of the GHN is exactly 2(n− 1), where n is the number of neurons in the network. That is, the GHN of n neurons can store 2(n− 1) bipolar sample patterns as its stable states when n is large, which has significantly improved the existing results on the asymptotic memory capacity of the GHN.
1465121
MFCC and SVM Based Recognition of Chinese Vowels
The recognition of vowels in Chinese speech is very important for Chinese speech recognition and understanding. However, it is rather difficult and there has been no efficient method to solve it yet. In this paper, we propose a new approach to the recognition of Chinese vowels via the support vector machine (SVM) with the Mel-Frequency Cepstral Coefficients (MFCCs) as the vowel’s features. It is shown by the experiments that this method can reach a high recognition accuracy on the given vowels database and outperform the SVM with the Linear Prediction Coding Cepstral (LPCC) coefficients as the vowel’s features.
6431369
A Neural Network Approach to Real-Time Pattern Recognition
This paper presents a new neural network approach to real-time pattern recognition on a given set of binary (or bipolar) sample patterns. The perceptive neuron of a binary pattern is defined and constructed as a binary neuron with a neighborhood perceptive field. Letting its hidden units be the respective perceptive neurons of the patterns, a three-layer forward neural network is constructed to recognize these patterns with minimum error probability in a noisy environment. The theoretical and simulation analyses show that the network is effective for pattern recognition and can be under strict real-time constraints.
26305122
The Stability of the Generalized Hopfield Networks in Randomly Asynchronous Mode
We present a study on the stability of the generalized Hopfield network in randomly asynchronous mode. First, the stability is investigated from the state space of the network. We introduce a concept of hole and define two kinds of stability in randomly asynchronous mode. By mathematical inductive method, we have proved that a generalized Hopfield network with non negative weights is strictly stable, that is, the network evolves to a simple hole-stable state with probability one when it starts at any initial state. For the general case, we made the simulation experiments on the networks with the number of neurons from 5 to 10. The empirical results have shown that almost any generalized Hopfield network with simple holes is strictly stable. Copyright 1997 Elsevier Science Ltd.
892651
A genetic algorithm for optimal 3-connected telecommunication network designs
In this paper, a genetic algorithm for three connected telecommunication network designs is proposed. The genetic algorithm uses a new approach to solution representation, in which constraints such as diameter and connectivity constraints can be easily encoded and two-point crossover with the operation of swapping duplicated nodes ensures solutions generated through genetic evolution are all feasible so that both checking of the constraints and repair mechanism can be avoided. Both efficiency and effectiveness are increased due to no checking of the constraints and no repair mechanism conducted. In fact, our design algorithm for three-connected network design can he extended to a high-connectivity network design.
16169209
Localized Low-Power Topology Control Algorithms in IEEE 802.15.4-Based Sensor Networks
Sensor networks have emerged as a promising technology with various applications, and power consumption is one of the key issues. Since each full function device can act as a coordinator or a device in IEEE 802.15.4 standard, 802.15.4-based sensor networks have various possible network topologies. In this paper, we try to construct network topologies with small number of coordinators while still maintaining network connectivity. By reducing the number of coordinators, the average duty cycle is reduced and the battery life is prolonged. Three topology control algorithms are proposed in this paper. Self-pruning is the simplest one with O(l) running time. Ordinal pruning significantly improves self-pruning in terms of power saving with O(n) running time. Layered pruning is a tradeoff between the first two pruning algorithms with O(radicn) running time and a little higher power consumption than ordinal pruning. Furthermore, all three algorithms are independent of the physical radio propagation characteristics
18902968
Fast interval-valued statistical interconnect modeling and reduction
Correlated interval representations of range uncertainty offer an attractive solution for approximating computations on statistical quantities. The key idea is to use finite intervals to approximate the essential mass of a pdf as it moves through numerical operators; the resulting compact interval-valued solution can be easily interpreted as a statistical distribution and efficiently sampled. This paper describes improved interval-valued algorithms for AWE/PRIMA model order reduction for tree-structured interconnect with correlated $RLC$ parameter variations. By moving to a faster interval-valued linear solver based on path-tracing ideas, and making more optimal trade-offs between interval- and scalar-valued computations, we can extract delay statistics roughly 10X faster than a classical Monte Carlo simulation loop, with accuracy to within 5%.
22258067
A principled approach for formative Web learning assessment and adaptive tutoring
Web based distance learning is a trend of instruction delivery. One of the most difficult challenges of such a learning mechanism is the assessment of students' learning criteria. It is hard to judge the behavior of a student since the instructor is separated spatially and temporally from the students. However, it is possible to rely on some Web based tools to keep track of a student's course attendance, as well as the navigation behavior of that student. In addition, the navigation behavior of an individual can be compared to those of others. Analysis can be conducted, and an interactive tutorial can be generated to assist the student with a poor score. The paper proposes such a mechanism, as well as its supporting system run on Windows browsers.
12652571
Implementation of a Pure P2p Collaboration Multiplatform and its Applications
Peer-to-Peer (P2P) computing offers many attractive features, such as collaboration, self-organization, load balancing, availability, fault tolerance and anonymity. However, it also faces many seri...