Enhanced Neural Network Modelling for a Real Multi-variable Chemical Process

Application of several Neural Network (NN) modelling techniques to model a Multi-Input Multi-Output (MIMO) nonlinear chemical process is investigated. The process is a laboratory scale chemical reactor with three inputs and three outputs. It typically represents industrial processes due to its nonlinearity, coupling effects and lack of a mathematical model. Different techniques have been used in collecting training data from the reactor. A novel method was used to select the model order and time-delay to determine the NN model input. A Radial Basis Function Network (RBFN) model was then developed. A Recursive Orthogonal Least Squares (ROLS) algorithm was applied as a numerically robust method to update the RBFN weight matrix. In this way, degradation of the modelling error due to ill-conditioning in the training data is avoided. Real data experiments show that the RBFN model developed has high accuracy.     

一种递归神经网络的快速并行算法

针对递归神经网络BP(Back Propagation)学习算法收敛慢的缺陷,提出一种新的递归神经网络快速并行学习算法.首先,引入递推预报误差(RPE)学习算法,并且证明了其稳定性;进一步地,为了克服RPE算法集中运算的不足,设计完整的并行结构算法.本算法将计算分配到神经网络中的每个神经元,完全符合神经网络的并行结构特点,也利于硬件实现.仿真结果表明,该算法比传统的递归BP学习算法具有更好的收敛性能.理论分析和仿真实验证明,该算法与RPE集中运算算法相比可以大大节省计算时间.     

一种基于遗传算法的模糊神经网络最优控制

通过对控制系统的过程模拟,提出一种模糊神经网络最优控制方案。离线化部分基于遗传算法,分三阶段实现模糊神经网络控制器结构和参数的优化。在线优化部分通过重构模糊神经网络控制器的去模糊化部分,进一步调整控制规则,实现在线去模糊优化。仿真结果表明该方案优于常模糊控制方案和基于专家经验的模糊神经网络控制方案。     

在线变结构模糊神经网络的自适应内模控制

本文提出一种在线变结构补偿模糊神经网络训练算法.该在线变结构算法,使得出的网络结构简单.并且由于该网络引入了补偿模糊神经元,使网络能对模糊规则进行在线的训练.将此算法应用到仿射非线性动态系统和大时滞线性动态系统的内模控制中.仿真结果表明,该方法能有效的控制动态过程,具有较好的自适应性和鲁棒性.     

On the Use of an Enhanced Hopfield Neural Model to Solve FMS Performance Optimization Problem

The performance of a Flexible Manufacturing System (FMS) is generally linked to its productivity and is often limited by poor use of available resources. One of the main goals in the automated factory environment is, therefore, the exploitation of resources to the full, in such a way as to optimize productivity. As widely documented in literature, this is a hard task on account of its computational complexity. For this reason a number of heuristic techniques are currently available, the best known of which are based on Event Graphs, which are a particular class of Petri Nets. The paper proposes a performance optimization technique which, although it is based on Event Graphs, applies algorithms which are different from traditional heuristic ones. More specifically, a novel neural model is used to solve the optimization problem. The neural model was obtained by making significant changes to a network that is well known in literature: the Hopfield network. The solution proposed is an original one and features several advantages against the most known heuristic approaches to the problem, the most important of which is the possibility of optimal or close to optimal solutions in a polynomial time, proportional to the size of the FMS. In addition, the possibility of simple, economical hardware implementation of the neural model favours its integration in the automated factory environment, allowing real-time supervision and optimization of productivity. The aim of the paper is to present the new neural model and its use in optimizing the performance of FMSs. A comparison of the neural approach with classical heuristic solutions and its real-time calculation capability, will also be treated in the paper.     

Application of a Recurrent Neural Network to Space Deversity in SDMA and CDMA Mobile Communication Systems

Linear and non-linear adaptive algorithms are investigated for Space Division Multiple Access (SDMA). SDMA is one of the emerging techniques for multiple access of users in mobile radio, which uses spatial distribution of users for their differentiation. The performance of the linear Square Root Kalman (SRK) algorithm for SDMA is compared to that of the non-linear Recurrent Neural Network (RNN) technique. The proposed SDMA-RNN technique is evaluated over Rician fading channels, and it shows improved Bit Error Rate (BER) performance in comparison with the linear SRK-based technique. The performance of SDMA-RNN is also compared with that of Code Division Multiple Access (CDMA) systems, showing that it could be used as a viable alternative scheme for multiple access of users. Finally, a Hybrid CDMA-SDMA system is proposed combining CDMA and SDMA-RNN systems. Hybrid CDMA-SDMA exhibits a very good potential for increase in the capacity and the performance of mobile communications systems.     

基于遗传算法的人工神经网络模型在冬小麦根系吸水模型中的应用

基于土壤水分与冠部数据,应用遗传算法优化人工神经网络模型的权值,将获得的冬小麦根长密度分布应用于根系吸水模型中,并进行了水分数值模拟,水分模拟效果整体较好,表明应用该方法可以为根系吸水模型提供准确的根系参数,并且较为方便,这对于根系吸水模型的建立及应用有着重要的意义。     

多价格竞争网络环境中移动代理的随机路径寻优

在多个提供商提供的多价格竞争网络环境中 ,端用户在访问内容提供商提供的内容时 ,需要与对多个网络提供商的网络性能和价格进行比较 ,以确定一个在一定 Qo S约束下的最优路径 .基于移动代理进行网络信息的采集 ,将网络延迟和费用成本均设为随机变量 ,在随机网络中建立了一个费用成本和延迟时间双重期望值目标的最小化模型 ,应用遗传算法对移动代理的路径进行了最优化求解 ,最后进行了仿真计算     

On the Berlekamp/Massey algorithm and counting singular Hankel matrices over a finite field

We derive an explicit count for the number of singular n×n$n\times n$ Hankel (Toeplitz) matrices whose entries range over a finite field with q$q$ elements by observing the execution of the Berlekamp/Massey algorithm on its elements. Our method yields explicit counts also when some entries above or on the anti-diagonal (diagonal) are fixed. For example, the number of singular n×n$n\times n$ Toeplitz matrices with 0’s on the diagonal is q²ⁿ⁻³+qⁿ⁻¹−qⁿ⁻²$q^{2n-3}+q^{n-1}-q^{n-2}$.