Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes

Humans and other animals have been shown to perform near-optimally in multi-sensory integration tasks. Probabilistic population codes (PPCs) have been proposed as a mechanism by which optimal integration can be accomplished. Previous approaches have focussed on how neural networks might produce PPCs from sensory input or perform calculations using them, like combining multiple PPCs. Less attention has been given to the question of how the necessary organisation of neurons can arise and how the required knowledge about the input statistics can be learned. In this paper, we propose a model of learning multi-sensory integration based on an unsupervised learning algorithm in which an artificial neural network learns the noise characteristics of each of its sources of input. Our algorithm borrows from the self-organising map the ability to learn latent-variable models of the input and extends it to learning to produce a PPC approximating a probability density function over the latent variable behind its (noisy) input. The neurons in our network are only required to perform simple calculations and we make few assumptions about input noise properties and tuning functions. We report on a neurorobotic experiment in which we apply our algorithm to multi-sensory integration in a humanoid robot to demonstrate its effectiveness and compare it to human multi-sensory integration on the behavioural level. We also show in simulations that our algorithm performs near-optimally under certain plausible conditions, and that it reproduces important aspects of natural multi-sensory integration on the neural level.     

神经网络在制备氮化硅多孔陶瓷中的应用

以凝胶注模法制备多孔氮化硅陶瓷正交试验结果作为样本,建立3层Back Propagation(BP)神经网络,并进行训练以预测陶瓷性能。通过附加试验值对建立的神经网络预测能力进行验证,证明该BP神经网络模型是有效的,能准确预测多孔氮化硅陶瓷性能。通过BP神经网络模型研究多孔氮化硅陶瓷性能的结果表明,随着固含量的增加,气孔率单调下降;固含量存在一优化值,此时陶瓷抗弯强度最大;单体含量越大,气孔率越大,而抗弯强度降低。     

基于细胞神经网络刀具磨损图像的预处理

提出了一种基于细胞神经网络的刀具磨损图像处理方法，通过设计细胞神经网络参数，运用细胞神经网络对刀具的二值图像平滑滤波，边缘提取，通过仿真证明该方法是有效的，由于细胞神经网络易于用VLSI实现并且并行处理速度快，因此该方法对刀具的磨损状态机器视觉检测中的图像处理具有实用意义。     

Modeling the compressive strength of molasses–cement sand system using design of experiments and back propagation neural network

Molasses, an eco-friendly and relatively cheap binder may be used as a substitute for chemical binders. For commercial exploitation of the molasses–cement sand system it is essential to generate models for predicting the properties of the sand mix from the composition. Central composite design is used to develop regression equations for predicting compressive strength of the sand mix when molasses is varied between 5.5% and 7.5% and cement between 2% and 4%. Though central composite design is an effective tool for studying the complex effects of number of independent variables on response factor it has quite a few limitations. Back propagation neural network is not only capable of modeling highly non-linear relationship using dispersed data in the solution domain but has a few advantages over the central composite design. But one of the major drawbacks of this network is that no theoretical basis exists to determine the number of hidden layers and number of neurons therein. Different configurations of BPNN have great effects on the predicted results. Back propagation neural networks of different configurations are trained. Results obtained form these networks are analyzed and compared with those obtained form regression equations and experiments. Guidelines for selecting the effective configuration of back propagation networks are proposed.     

Fractional power series neural network for solving delay fractional optimal control problems

ABSTRACT

In this paper, we develop a numerical method for solving the delay optimal control problems of fractional-order. The fractional derivatives are considered in the Caputo sense. The process begins with the assumption that the problem is first transformed into an equivalent problem with a fractional dynamical system without delay, using a Padé approximation. We then try to approximate the solution of the Hamiltonian conditions based on the Pontryagin minimum principle. The main feature is to implement nonlinear polynomial expansions in a neural network adaptive structure. The transfer functions of the employed neural network follow a fractional power series. The proposed technique does not use sigmoid or hyperbolic tangent nonlinear transfer functions commonly adopted in conventional neural networks at the output. Instead, linear transfer functions are employed which lead to explicit fractional power series formulae for the fractional optimal control problem. To do this, we use trial solutions for the states, Lagrange multipliers and control functions where these trial solutions are constructed by fractional power series neural network model. We then minimise the error function using an unconstrained optimisation scheme where weight parameters (or coefficients of the series) and biases associated with all neurons are unknown. Some numerical examples are given to illustrate the effectiveness of the proposed scheme.     

A spiking half-cognitive model for classification

This paper describes a spiking neural network that learns classes. Following a classic Psychological task, the model learns some types of classes better than other types, so the net is a spiking cognitive model of classification. A simulated neural system, derived from an existing model, learns natural kinds, but is unable to form sufficient attractor states for all of the types of classes. An extension of the model, using a combination of singleton and triplets of input features, learns all of the types. The models make use of a principled mechanism for spontaneous firing, and a compensatory Hebbian learning rule. Combined, the mechanisms allow learning to spread to neurons not directly stimulated by the environment. The overall network learns the types of classes in a fashion broadly consistent with the Psychological data. However, the order of speed of learning the types is not entirely consistent with the Psychological data, but may be consistent with one of two Psychological systems a given person possesses. A Psychological test of this hypothesis is proposed.     

Analysis–adjustment–synthesis networks

This paper presents a novel neural network architecture, analysis–adjustment–synthesis network (AASN), and tests its efficiency and accuracy in modelling non-linear function and classification. The AASN is a composite of three sub-networks: analysis sub-network; adjustment sub-network; and synthesis sub-network. The analysis sub-network is a one-layered network that spreads the input values into a layer of ‘spread input neurons’. This synthesis sub-network is a one-layered network that spreads the output values back into a layer of ‘spread output neurons’. The adjustment sub-network, between the analysis sub-network and the synthesis sub-network, is a standard multi-layered network that operates as the learning mechanism. After training the adjustment sub-network in recalling phase, the synthesis sub-network receives the output values of spread output neurons and synthesizes them into output values with a weighted-average computation. The weights in the weighted-average computation are deduced from the method of Lagrange multipliers. The approach is tested using four function mapping problems and one classification problem. The results show that combining the analysis sub-network and the synthesis sub-network with a multi-layered network can significantly improve a network's efficiency and accuracy.     

模糊神经网络在感应电机矢量控制中的应用

本文提出了一种基于模糊神经网络速度控制器（FNNC）的感应电机矢量控制系统,兼具模糊逻辑处理不确定信息的能力和神经网络的自学习能力,阐明了神经网络的结构设计、样本选取及训练方法。人工神经网络（ANN）的初始权值和阈值通过离线学习得到,模糊逻辑规则通过专家经验总结。仿真结果表明采用所提出的模糊神经网络的感应电机矢量控制系统,转速响应快,跟踪性能好,稳态误差大大减小,有效提高了系统的性能。     

Adaptive network based inference system for estimation of flank wear in end-milling

The focus of this paper is to develop a reliable method to predict flank wear during end-milling process. A neural-fuzzy scheme is applied to perform the prediction of flank wear from cutting force signals. In this contribution we also discussed the construction of a ANFIS system that seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the neural network. Machining experiments conducted using the proposed method indicate that using an appropriate maximum force signals, the flank wear can be predicted within 4% of the actual wear for various end-milling conditions.     

Connectionist learning: A comparison of neural networks and an optical thin-film multilayer model

Current work on connectionist models has been focused largely on artificial neural networks that are inspired by the networks of biological neurons in the human brain. However, there are also other connectionistarchitectures that differ significantly from this biological exemplar. We proposed a novel connectionist learning architecture inspired by the physics associated with optical coatings of multiple layers of thin-films in a previous paper (Li and Purvis 1999, Annals of Mathematics and Artificial Intelligence, 26: 1-4). The proposed model differs significantly from the widely used neuron-inspired models. With thin-film layer thicknesses serving as adjustable parameters (as compared with connection weights in a neural network) for the learning system, the optical thin-film multilayer model (OTFM) is capable of approximating virtually any kind of highly nonlinear mappings. The OTFM is not a physical implementation using optical devices. Instead, it is proposed as a new connectionist learning architecture with its distinct optical properties as compared with neural networks. In this paper we focus on a detailed comparison of neural networks and the OTFM (Li 2001, Proceedings ofINNS-IEEE International Joint Conference on Neural Networks, Washington, DC, pp. 1727-1732). We describe the architecture of the OTFM and show how it can be viewed as a connectionist learning model. We then present experimental results on solving a classification problem and a time series prediction problem that are typical of conventional connectionist architectures to demonstrate the OTFM's learning capability.     

刀具磨损监测的一种数据处理方法

采用声发射传感器采集刀具切削时的信号,提出了一种基于BP神经网络识别刀具磨损程度的方法。该方法将原始声发射信号经高通滤波后直接输入到BP神经网络中进行训练,依靠神经网络的非线性映射能力,使神经网络对不同磨损程度刀具产生的信号进行分类,并能准确判别未知信号所属类别。与传统方法相比,该方法省去了人工提取特征值这一费时费力的环节。研究了神经元个数对神经网络的训练和识别的影响,提高了神经网络的识别精度。实验结果表明,该方法可以准确地预测刀具磨损程度。     

板材拉深成形智能化控制过程中摩擦系数的识别

提出在板材拉深成形过程中确定摩擦系数的一种新方法。在阐述解析法描述摩擦系数的基础上,利用人工神经网络来实现对摩擦系数的识别,以便根据摩擦系数的波动,随时调整控制参数,以最佳的工艺参数来完成板材拉深成形的智能化控制过程。     

基于FPGA的网络型运动控制芯片设计

针对运动控制系统网络化、集成化的需求,提出了一款基于FPGA的网络型运动控制芯片设计方案.在单片FPGA中实现了以太网网络芯片的控制、数据协议解析、运动控制精插补器和编码器反馈接口,与上位机、网络主站构成运动控制网络,可以驱动步进电机、位置模式伺服驱动器,实现高效集成的网络化运动控制,适合在多通道复合数控机床、多轴运动控制等场合应用.仿真与实验验证了方案的可行性.     

Study on twin-wire pulsed MIG welding power supply based on DSP and CAN

A design proposal for twin-wire pulsed MIG welding power supply based on DSP (digital signal processor)and CAN (controller area network ) is put forward.By use of the CAN bus,the synergic control between the master and slave power supplies can be realized.And in this way,their peak currents can be guaranteed to be alternative and the interference between the two arcs can be decreased efficiently.The hardware design,software design and relative tests are provided in this paper.Tests show that the power supply can meet the design requirements of twin-wire welding.     

Self-organization of Multiple Winner-take-all Neural Networks

In this paper, analysis of the information content of discretely firing neurons in unsupervised neural networks is presented, where information is measured according to the network's ability to reconstruct its input from its output with minimum mean square Euclidean error. It is shown how this type of network can self-organize into multiple winner-take-all subnetworks, each of which tackles only a low-dimensional subspace of the input vector. This is a rudimentary example of a neural network that effectively subdivides a task into manageable subtasks.     

基于电弧声波特征的CO2焊接飞溅预测

在对短路过渡CO2 焊电弧声波信号的时频特征及其与过渡过程、焊接飞溅的相关性分析的基础上 ,利用小波变换的分析方法提取不同频段上的声波能量作为表征飞溅大小的特征向量 ,通过神经网络模型建立特征向量到飞溅量的映射模型 ,从而对CO2 焊接飞溅量的预测。结果表明 ,利用电弧声波信号能够正确地预测焊接飞溅 ,是实现焊接质量在线监控的新途径     

基于BP算法的逆变点焊电源模糊神经网络控制研究

引入动量因子对常规BP学习算法进行了改进.在分析模糊神经网络控制模型的基础上,针对模糊神经网络规则多、训练时间长的缺点,采用了给模糊控制规则增加阈值,减少网络训练运算量的优化方法.最后将此优化方法和改进的训练算法应用到逆变点焊电源模糊神经网络(FNN)恒电流控制系统中,通过使用MATLAB语言编程,对该系统进行了仿真分析.仿真结果表明,动量因子的引入不但减小了BP算法学习过程的振荡趋势,加快了收敛速度,而且较好解决了BP网络容易陷入局部极小点的缺陷.模糊规则阈值的引入,有效减少了网络的训练时间.     

Creating hierarchical categories using cell assemblies

Highly recurrent neural networks can learn reverberating circuits called Cell Assemblies (CAs). These networks can be used to categorize input, and this paper explores the ability of CAs to learn hierarchical categories. A simulator, based on spiking fatiguing leaky integrators, is presented with instances of base categories. Learning is done using a compensatory Hebbian learning rule. The model takes advantage of overlapping CAs where neurons may participate in more than one CA. Using the unsupervised compensatory learning rule, the networks learn a hierarchy of categories that correctly categorize 97% of the basic level presentations of the input in our test. It categorizes 100% of the super-categories correctly. A larger hierarchy is learned that correctly categorizes 100% of base categories, and 89% of super-categories. It is also shown how novel subcategories gain default information from their super-category. These simulations show that networks containing CAs can be used to learn hierarchical categories. The network then can successfully categorize novel inputs.     

均匀设计法、神经网络和遗传算法结合在内高压成形工艺参数优化中的应用

内高压成形技术是以轻量化和一体化为特征的一种空心变截面轻体构件的先进制造技术。目前,内高压成形技术越来越受到人们的关注,特别是汽车制造企业。管材的内高压成形过程与很多因素有关,其中施加在管件内部的压力与轴向进给量之间的配比关系尤为重要,对两者的匹配关系进行优化是内高压成形面临的重要课题。传统的优化方法需要大量的模拟计算,耗时多且不易掌握。针对这一问题,该文提出了将均匀设计法、神经网络和遗传算法相结合进行参数优化,既利用了均匀设计试验的均匀可靠性,又运用神经网络的非线性映射、网络推理和预测功能,最后发挥遗传算法的全局优化特性,得出了最优结果,并直接为实际生产提供了可靠的参数依据。