378573
The Object Perceptron Learning Algorithm on Generalised Hopfield Networks for Associative Memory
We present a study of generalised Hopfield networks for associative memory. By analysing the radius of attraction of a stable state, the Object Perceptron Learning Algorithm (OPLA) and OPLA scheme are proposed to store a set of sample patterns (vectors) in a generalised Hopfield network with their radii of attraction as large as we require. OPLA modifies a set of weights and a threshold in a way similar to the perceptron learning algorithm. The simulation results show that the OPLA scheme is more effective for associative memory than both the sum-of-outer produce scheme with a Hopfield network and the weighted sum-of-outer product scheme with an asymmetric Hopfield network.
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.
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.
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.
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.
1826840
Towards global routing with RLC crosstalk constraints
Conventional global routing minimizes total wire length and congestion. Experiments using large industrial benchmark circuits show that up to 24% of nets in such routing solutions may have RLC crosstalk violations at 3GHz clock. We develop an extremely efficient length-scaled Keff (LSK) model that has a high fidelity for long-range RLC crosstalk. We formulate an extended global routing problem (denoted as GSINO) to consider simultaneous shield insertion and net ordering with RLC crosstalk constraints, then propose an effective three-phase GSINO algorithm. The GSINO algorithm completely eliminates the RLC crosstalk violations, and has small area and wire length overhead compared to conventional routing.
19626066
A Web-Based Teacher Assisting Agent System
In the conventional teaching, teaching assistances (TAs) play importance roles in supporting a teacher’s teaching activities. This paper presents a Web-based teacher assisting agent system in which a set of automated programs, so called agents in this context, is developed. It is described that how the agents are generated, where they reside, and how they work independently or collaborate with each other to assist a teacher. It is emphasized that a later developed working agent can be easily incorporated into the system under the developed system framework. It is addressed that both agent persistency and mobility in the system can be achieved by the proposed mechanisms.
18106069
TOMSCOP - a synchronous P2P collaboration platform over JXTA
We focus on design and implementation of a synchronous P2P (peer-to-peer) collaboration platform called TOMSCOP (Technology Of Multiuser Synchronous Collaboration Platform). Based on the elementary peer group services offered by the JXTA general framework, TOMSCOP provides the extra four types of services: synchronous message transportation, peer room administration, peer communication support and application space management. By using the four services, different kinds of shared applications for various specific purposes can be relatively easily developed and associated collaborative cyber spaces or communities can be quickly built across the JXTA virtual network overlaid on top of the existing physical networks.
10392733
Pre-routing estimation of shielding for RLC signal integrity
The formula-based K/sub eff/ model is a figure of merit for inductive coupling, and has been used to solve the simultaneous shield insertion and net ordering (SINO) and simultaneous signal and power routing (SPR) problems. The authors first show that the K/sub eff/ model has a high fidelity compared to the SPICE-computed noise under an accurate RLC circuit model. We then develop simple yet accurate formulae to estimate numbers of shields needed by optimal SINO solutions under the K/sub eff/ model. Extensive experiments show that our pre-routing estimation has errors less than 10% compared to solutions given by detailed SINO algorithms. These formulae can be used effectively as a pre-routing congestion estimation for layout planning and synthesis.
2850355
An Optimal Node Selection Scheme in Multi-hop Wireless Network
In this paper, a cooperative wireless multi-hop network is considered, cooperative diversity can be realized by allowing nodes close proximity to cooperate in transmission. In order to group receiver nodes into a cluster, it is necessary to select appropriate nodes. We introduce an optimal node selection algorithm based on MIMO antenna selection, this algorithm, combining with cooperative transmission strategy, gives an optimal node selection algorithm using convex optimization theory. Theoretical analysis and simulation indicate that this scheme can give good performance of maximizing the channel capacity through optimal node selection.