恒温恒湿空调房间的解耦控制方案

采用机制分析和实验分析相结合的方法，建立恒温恒湿空调系统的数学模型，推导四输入、四输出的传递函数矩阵，采用前馈补偿法设计解耦补偿器，使恒温恒湿空调系统的传递函数矩阵变换为对角矩阵，从而解除各个控制回路之间的耦合，并通过仿真进行验证。结果表明，该解耦控制器在恒温恒湿空调系统中的应用效果较好，改善和提高系统的品质和性能。     

多层前馈神经网络的面向对象的程序设计框架

提出多层前馈神经网络的一种面向对象的程序设计框架，其中包括表示前馈神经网络中的神经元、权值的数据成员，以及完成网络的构造、初始化、学习和输出值计算等功能的成员方法，并用支持面向对象的程序设计语言Java实现了这一框架。模式分类和函数近似等问题的解决说明了这一前馈神经网络的面向对象的程序设计框架的有效性。     

一种新型前馈自动增益控制光接收机的研究

自动增益控制模拟光接收机广泛应用于光纤有线电视、光纤直播卫星电视、无线通讯及雷达等射频微波信号光传输,是实现大动态范围光载波信号高质量接收必不可少的一项技术.我们采用一种入射光功率电平检测与处理的新方法,对模拟光接收机的增益进行控制,将接收机输出电平控制在要求的范围内,实现了一种新型的无射频反馈电路的自动增益控制光接收机.     

热电联产机组集成热泵实现热电解耦的潜力与能耗特性分析

在热电联产机组中集成机械式热泵可以实现热电解耦,并影响机组的能耗特性。以某350MW热电联产机组为例,建立了供热机组变工况计算模型以及机械式热泵供热计算模型,比较了热电联产机组采用抽汽供热和压缩式热泵供热时最低电负荷率及煤耗量的差别,同时研究了热泵性能系数、电负荷率、热负荷率对机组能耗特性的影响规律。结果表明：在基准工况下,压缩式热泵供热较热电联产节能,且其机组的电负荷可调节范围更大。压缩式热泵供热相比抽汽供热减少的煤耗量随着热泵实际性能系数α_COP1、热负荷率增大而增大,随着电负荷率增大而减少,α_COP1为6.119,热负荷率为2.5,电负荷率为0.8时,节煤率为2.60%。     

双并联前向神经网络的误差分析

分析了作者提出的双并联前向神经网络（ＤＰＦＮＮ）有限精度实现的误差界，并给出多种有限精度运算实现的仿真结果，其结果对神经网络的有限精度实现和工程应用设计具有一定的指导意义     

多层前向神经网络结构的研究进展*

本文综述了的牢来多层前向神经网络结构的研究进展，总结了多怪前向网络结构调整的各种方法，并对各种方法进行了比较分析，最后提出了若干研究的问题。     

Optimization of boiler control to improve the load-following capability of power-plant units

The capability to perform fast load changes has been an important issue in the power market, and will become increasingly more so due to the increasing commercialization of the European power market. An optimizing control system for improving the loadfollowing capability of power-plant units has therefore been developed. The system is implemented as a complement, producing control signals to be added to those of the existing boiler control system, a concept which has various practical advantages in terms of implementation and commissioning. The optimizing control system takes account of the multivariable and load-dependent nonlinear characteristics of the boiler process, as a scheduled LQG controller with feedforward action is utilized. The LQG controller improves the control of critical process variables, making it possible to increase the load-following capability of a specific plant. Field tests on a 265 MW coal-fired power-plant unit reveals that the maximum allowable load gradient that can be imposed on the plant, can be increased from 4 to 8 MW/min.     

Wavelet networks for sensor signal classification in flank wear assessment

It is known that the force and vibration sensor signals in a turning process are sensitive to the gradually increasing flank wear. Based on this fact, this paper investigates a flank wear assessment technique in turning through force and vibration signals. Mainly to reduce the computational burden associated with the existing sensor-based methods for flank wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters (that represent signal features) and the signal-interpretation parameters (that are equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to flank wear classes. A novel training algorithm for such a network is developed. The performance of this n ew method is compared with a previously developed flank wear assessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring.     

Neural and Neurofuzzy FELA Adaptive Robot Control Using Feedforward and Counterpropagation Networks

In this paper, the application of neural networks and neurofuzzy systems to the control of robotic manipulators is examined. Two main control structures are presented in a comparative manner. The first is a Counter Propagation Network-based Fuzzy Controller (CPN-FC) which is able to self-organize and correct on-line its rule base. The self-tuning capability of the fuzzy logic controller is attained by taking advantage of the structural equivalence between the fuzzy logic controller and a counterpropagation network. The second control structure is a more familiar neural adaptive controller based on a feedforward (MLP) network. The neural controller learns the inverse dynamics of the robot joints, and gradually eliminates the model uncertainties and disturbances. Both schemes cooperate with the computed torque control algorithm, and in that way the reduction of their complexity is achieved. The ability of adaptive fuzzy systems to compete with neural networks in difficult control problems is demonstrated. A sufficient set of numerical results is included.     

PID神经元网络对强耦合带时延多变量系统的解耦控制

提出了一种新的神经元网络－ＰＩＤ神经元网络，它是由比例元、积分元和微分元构成的多层神经元网络，它可以用于控制强耦合带时延多变量系统，并使系统具有良好的动态和静态性能。