Shoujue Wang

A More Complex Neuron in Biomimetic Pattern Recognition

Biomimetic pattern recognition has been proposed for several years, but the discussion of its neuron was not very wide and deep. In this paper, we propose a new more complex neuron named Psi3-neuron and give the application in the last part of the paper

A Face Detection Algorithm Based on High Dimensional Space Geometry

In this paper, a face detection algorithm which is based on high dimensional space geometry has been proposed. Then after the simulation experiment of Euclidean distance and the introduced algorithm, it was theoretically analyzed and discussed that the proposed algorithm has apparently advantage over the Euclidean distance. Furthermore, in our experiments in color images, the proposed algorithm even gives more surprises

Biomimetic Pattern Recognition for Speaker-Independent Speech Recognition

In speaker-independent speech recognition, the disadvantage of the most diffused technology (hidden Markov models) is not only the need of many more training samples, but also long train time requirement. This paper describes the use of biomimetic pattern recognition (BPR) in recognizing some Mandarin speech in a speaker-independent manner. The vocabulary of the system consists of 15 Chinese dishs names. Neural networks based on multi-weight neuron (MWN) model are used to train and recognize the speech sounds. Experimental results are presented to show that the system, which can carry out real time recognition of the persons from different provinces speaking common Chinese speech, outperforms HMMs especially in the cases of samples of a finite size

Study on text-dependent speaker recognition based on biomimetic pattern recognition

We studied the application of Biomimetic Pattern Recognition to speaker recognition. A speaker recognition neural network using network matching degree as criterion is proposed. It has been used in the system of text-dependent speaker recognition. Experimental results show that good effect could be obtained even with lesser samples. Furthermore, the misrecognition caused by untrained speakers occurring in testing could be controlled effectively. In addition, the basic idea “cognition” of Biomimetic Pattern Recognition results in no requirement of retraining the old system for enrolling new speakers.

Computational Information Geometry And Its Applications

Digitization is the main feature of modern information science. Conjoining the digits and the coordinates, the relation between information science and high-dimensional space is consanguineous, and the information issues are transformed to the geometry problems in some high-dimensional spaces. From this basic idea, we propose computational information geometry (CIG) to make information analysis and processing. Two kinds of applications of CIG are given, which are blurred image restoration and pattern recognition. Experimental results are satisfying. And in this paper, how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is also introduced. Lots of the algorithms have been realized using software

The Structure and Function of Neurons with Variable Nonlinear Transfer Function

One novel neuron with variable nonlinear transfer function is firstly proposed, It could also be called as subsection transfer function neuron. With different transfer function components, by virtue of multi-thresholded, the variable transfer function neuron switch on among different nonlinear excited state. And the comparison of outputs transfer characteristics between it and single-thresholded neuron is illustrated, with some practical application experiments on bi-level logic operation, at last the simple comparison with conventional BP, RBF, and even DBF NN is taken to expect the development foreground on the variable neuron. The novel nonlinear transfer function neuron could implement the random nonlinear mapping relationship between input layer and output layer, which could make variable transfer function neuron have one much wider applications on lots of research realm such as function approximation, pattern recognition, data compress and so on

Determining of the Delay Time for a Heating Ventilating and Air-Conditioning Plant Using Two Weighted Neural Network Approach

This paper presents an two weighted neural network approach to determine the delay time for a heating, ventilating and air-conditioning (HVAC) plan to respond to control actions. The two weighted neural network is a fully connected four-layer network. An acceleration technique was used to improve the General Delta Rule for the learning process. Experimental data for heating and cooling modes were used with both the two weighted neural network and a traditional mathematical method to determine the delay time. The results show that two weighted neural networks can be used effectively determining the delay time for AVAC systems.

A Design Method of Associative Memory Model with Expecting Fault-Tolerant Field

A design method of associative memory model with expecting fault-tolerant field is proposed.The benefit of this method is to make the designed associative memory model memory sample fault-tolerant field which implements the hoped situation. For any different P samples in ndimensional binary information space Dn= [1, i¾? 1]nand any the pcompartmentalization C 1 ,C 2 ,...,C p of Dn, an associative memory model with expecting fault-tolerant field C 1 ,C 2 ,...,C p can be designed by the method. The method better solves the difficult synthesis problems of associative memory models.

High-Dimensional space geometrical informatics and its applications to image restoration

With a view to solve the problems in modern information science, we put forward a new subject named High-Dimensional Space Geometrical Informatics (HDSGI). It builds a bridge between information science and point distribution analysis in high-dimensional space. A good many experimental results certified the correctness and availability of the theory of HDSGI. The proposed method for image restoration is an instance of its application in signal processing. Using an iterative “further blurring-debluring-further blurring” algorithm, the deblured image could be obtained.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model — Multi-Degree-of-Freedom Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points set’s topological character in the feature space, which is different from the traditional “separation” method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.