Batch-to-batch control of fed-batch processes using control-affine feedforward neural network

A control strategy for fed-batch processes is proposed based on control affine feed-forward neural network (CAFNN). Many fed-batch processes can be considered as a class of control affine nonlinear systems. CAFNN is constructed by a special structure to fit the control affine system. It is similar to a multi-layer feed-forward neural network, but it has its own particular feature to model the fed-batch process. CAFNN can be trained by a modified Levenberg–Marquardt (LM) algorithm. However, due to model-plant mismatches and unknown disturbances, the optimal control policy calculated based on the CAFNN model may not be optimal when applied to the fed-batch process. In terms of the repetitive nature of fed-batch processes, iterative learning control (ILC) can be used to improve the process performance from batch to batch. Due to the special structure of CAFNN, the gradient information of CAFNN can be computed analytically and applied to the batch-to-batch ILC. Under the ILC strategy from batch to batch, endpoint product qualities of fed-batch processes can be improved gradually. The proposed control scheme is illustrated on a simulated fed-batch ethanol fermentation process.     

Neural Network Algorithm for Designing FIR Filters Utilizing Frequency-Response Masking Technique

This paper presents a new joint optimization method for the design of sharp linear-phase finite-impulse response (FIR) digital filters which are synthesized by using basic and multistage frequency-response-masking(FRM) techniques.The method is based on a batch back-propagation neural network algorithm with a variable learning rate mode.We propose the following two-step optimization technique in order to reduce the complexity.At the first step,an initial FRM filter is designed by alternately optimizing th...     

Receding horizon estimation to networked control systems with multirate scheme

This paper investigates the problem of receding horizon state estimation for networked control systems (NCSs) with random network-induced delays less than one sample period, which are formulated as multirate control systems. Based on a batch of recent past slow rate measurements in a finite horizon window, the initial state estimation in this window is solved by minimizing a receding-horizon objective function, and then the fast rate state estimations are calculated by the prediction of dynamic equation to compensate for the network-induced time delays. Furthermore, convergence results and unbiasedness properties are analyzed. An upper bound of estimation error is presented under the assumption of bounded disturbances acting on the system and measurement equations. A simulation example shows the effectiveness of the proposed method. Supported by the National Natural Science Foundation of China (Grant Nos. 60774015, 60674018, 60825302), the National High-Tech Research & Development Program of China (Grant No. 2006AA04Z173), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20060248001), and partly by Shanghai Natural Science Foundation (Grant No. 07JC14016)     

New delay-dependent criterion for the stability of recurrent neural networks with time-varying delay

This paper is concerned with the global asymptotic stability of a class of recurrent neural networks with interval time-varying delay. By constructing a suitable Lyapunov functional, a new criterion is established to ensure the global asymptotic stability of the concerned neural networks, which can be expressed in the form of linear matrix inequality and independent of the size of derivative of time varying delay. Two numerical examples show the effectiveness of the obtained results. Supported by the National Natural Science Foundation of China (Grant Nos. 60534010, 60728307, 60774048, 60774093), the Program for Cheung Kong Scholars and Innovative Research Groups of China (Grant No. 60521003) and the National High-Tech Research & Development Program of China (Grant No. 2006AA04Z183), China Postdoctoral Sciencer Foundation (Grant No. 20080431150), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 200801451096)     

Min-max model predictive control for constrained nonlinear systems via multiple LPV embeddings

A min-max model predictive control strategy is proposed for a class of constrained nonlinear system whose trajectories can be embedded within those of a bank of linear parameter varying (LPV) models. The embedding LPV models can yield much better approximation of the nonlinear system dynamics than a single LTV model. For each LPV model, a parameter-dependent Lyapunov function is introduced to obtain poly-quadratically stable control law and to guarantee the feasibility and stability of the original nonlinear system. This approach can greatly reduce computational burden in traditional nonlinear predictive control strategy. Finally a simulation example illustrating the strategy is presented. Supported by the National Natural Science Foundation of China (Grant Nos. 60774015, 60825302, 60674018), the National High-Tech Research & Development Program of China (Grant No. 2007AA041403), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20060248001), and partly by Shanghai Natural Science Foundation (Grant No. 07JC14016)     

A novel algorithm for explicit optimal multi-degree reduction of triangular surfaces

This paper introduces the algebraic property of bivariate orthonormal Jacobi polynomials into geometric approximation. Based on the latest results on the transformation formulae between bivariate Bernstein polynomials and Jacobi polynomials, we naturally deduce a novel algorithm for multi-degree reduction of triangular B~zier surfaces. This algorithm possesses four characteristics： ability of error forecast, explicit expression, less time consumption, and best precision. That is, firstly, whether there exists a multi-degree reduced surface within a prescribed tolerance is judged beforehand; secondly, all the operations of multi-degree reduction are just to multiply the column vector generated by sorting the series of the control points of the original surface in lexicographic order by a matrix; thirdly, this matrix can be computed at one time and stored in an array before processing degree reduction; fourthly, the multi-degree reduced surface achieves an optimal approximation in the norm L2. Some numerical experiments are presented to validate the effectiveness of this algorithm, and to show that the algorithm is applicable to information processing of products in CAD system.     

SKY: efficient peer-to-peer networks based on distributed Kautz graphs

Many proposed P2P networks are based on traditional interconnection topologies. Given a static topology, the maintenance mechanism for node join/departure is critical to designing an efficient P2P network. Kautz graphs have many good properties such as constant degree, low congestion and optimal diameter. Due to the complexity in topology maintenance, however, to date there have been no effective P2P networks that are proposed based on Kautz graphs with base > 2. To address this problem, this paper presents the “distributed Kautz (D-Kautz) graphs”, which adapt Kautz graphs to the characteristics of P2P networks. Using the D-Kautz graphs we further propose SKY, the first effective P2P network based on Kautz graphs with arbitrary base. The effectiveness of SKY is demonstrated through analysis and simulations. Supported partially by the National Natural Science Foundation of China (Grant Nos. 60673167 and 60703072), the Hunan Provincial Natural Science Foundation of China (Grant No. 08JJ3125), and the National Basic Research Program of China (973) (Grant No. 2005CB321801)     

Removing local irregularities of triangular meshes with highlight line models

The highlight line model is a powerful tool in assessing the quality of a surface. It increases the flexibility of an interactive design environment. In this paper, a method to generate a highlight line model on an arbitrary triangular mesh is presented. Based on the highlight line model, a technique to remove local shape irregularities of a triangular mesh is then presented. The shape modification is done by solving a minimization problem and performing an iterative procedure. The new technique improves not only the shape quality of the mesh surface, but also the shape of the highlight line model. It provides an intuitive and yet suitable method for locally optimizing the shape of a triangular mesh. Supported by National Science Foundation of China (Grant Nos. 60533070, 60625202), National Basic Research Program of China (Grant No. 2004CB719400), National High-Tech Research & Development Program of China (Grant No. 2007AA040401), Fok Ying Tung Education Foundation (Grant No. 111070), National Science Foundation of USA (Grant Nos. DMI-0422126, DMS-0310645), and Kentucky Science & Technology Corporation (Grant No. COMM-Fund-712)     

Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis

A new modeling approach for nonlinear systems with rate-dependent hysteresis is proposed. The approach is used for the modeling of the giant magnetostrictive actuator, which has the rate-dependent nonlinear property. The models built are simpler than the existed approaches. Compared with the experiment result, the model built can well describe the hysteresis nonlinear of the actuator for input signals with complex frequency. An adaptive direct inverse control approach is proposed based on the fuzzy tree model and inverse learning and special learning that are used in neural network broadly. In this approach, the inverse model of the plant is identified to be the initial controller firstly. Then, the inverse model is connected with the plant in series and the linear parameters of the controller are adjusted using the least mean square algorithm by on-line manner. The direct inverse control approach based on the fuzzy tree model is applied on the tracing control of the actuator by simulation. The simulation results show the correctness of the approach. Supported by the National Natural Science Foundation of China (Grant No. 60534020), the National Basic Research Program of China (Grant No. G2002cb312205-04), the Research Fund for the Doctoral Program of Higher Education (Grant No. 20070006060), and the Key Subject Foundation of Beijing (Grant Nos. XK100060526, XK100060422)     

Optimal parallel verification of minimum spanning trees in logarithmic time

We present the first optimal parallel algorithms for the verification and sensitivity analysis of minimum spanning trees. Our algorithms are deterministic and run inO(logn) time and require linear-work in the CREW PRAM model. These algorithms are used as a subroutine in the linear-work randomized algorithm for finding minimum spanning trees of Cole, Klein, and Tarjan. Research partially supported by a National Science Foundation Graduate Fellowship and by DIMACS (Center for Discrete Mathematics and Theoretical Computer Science), a National Science Foundation Science and Technology Center, Grant No. NSF-STC88-09648. Research at Princeton University was partially supported by the National Science Foundation, Grant No. CCR-8920505, the Office of Naval Research, Contract No. N00014-91-J-1463, and by DIMACS (Center for Discrete Mathematics and Theoretical Computer Science), a National Science Foundation Science and Technology Center, Grant No. NSF-STC88-09648.     

Globally exponentially attractive sets of the family of Lorenz systems

In this paper, the concept of globally exponentially attractive set is proposed and used to consider the ultimate bounds of the family of Lorenz systems with varying parameters. Explicit estimations of the ultimate bounds are derived. The results presented in this paper contain all the existing results as special cases. In particular, the critical cases, b→ 1^＋ and a→0^＋, for which the previous methods failed, have been solved using a unified formula.     

Multiple accumulated-crossover parallel concatenated SPC codes

By constructing an accumulated-crossover relationship in multiple parallel concatenated single parity check (M-PC-SPC) codes, a class of error-correcting codes, termed multiple accumulated-crossover parallel concatenated single parity check (M-ACPC-SPC) codes, is proposed. M-ACPC-SPC codes possess linear encoding complexity and can be decoded iteratively with low complexity by the sum-product algorithm (SPA). Simulation results show that M-ACPC-SPC codes have lower error floors than M-PCSPC codes with the same dimension, and when the dimension is 5, M-ACPC-SPC codes achieve bit error rate (BER) better than (3, 6) regular low density parity check (LDPC) codes. Supported by the National High-Tech Research & Development Program of China (Grant No. 2007AA01Z288), the National Science Fund for Distinguished Young Scholars (Grant No. 60725105), the Program for Changjiang Scholars and Innovative Research Team in University and the 111 Project (Grant No. B08038)     

A point-based rendering approach for real-time interaction on mobile devices

Mobile device is an important interactive platform. Due to the limitation of computation, memory, display area and energy, how to realize the efficient and real-time interaction of 3D models based on mobile devices is an important research topic. Considering features of mobile devices, this paper adopts remote rendering mode and point models, and then, proposes a transmission and rendering approach that could interact in real time. First, improved simplification algorithm based on MLS and display resolution of mobile devices is proposed. Then, a hierarchy selection of point models and a QoS transmission control strategy are given based on interest area of operator, interest degree of object in the virtual environment and rendering error. They can save the energy consumption. Finally, the rendering and interaction of point models are completed on mobile devices. The experiments show that our method is efficient. Supported by the National Natural Science Foundation of China (Grant No. 60873159), the Program for New Century Excellent Talents in University (Grant No. NCET-07-0039), the National High-Tech Research & Development Progrom of China (Grant No. 2006AA01Z333)     

Power allocation for non-orthogonal decode-and-forward cooperation protocol

This paper studies the power allocation problem for the non-orthogonal decode-and-forward (NDF) cooperation protocol with selection relaying. With the availability of the magnitudes of all channel gains at the source, the power allocation is explored that maximizes the mutual information between the source and destination subject to a total power constraint. The minimum power that avoids the outage of the relay is set as a condition, under which the power allocation problem becomes one of selecting the optimal one from several allocation factor triplets. It is shown that the power allocation scheme can provide considerable performance gain, and the non-orthogonal cooperation protocol is superior to the orthogonal protocol and direct transmission. Supported by the Project of Natural Science Foundation of Jiangsu Province (Grant No. BK2006701), the National Natural Science Foundation of China (Grant No. 60672079), and the National High-Tech Research & Development Program of China (Grant No. 2007AA01Z267)     

Adjoint code generator

The adjoint code generator (ADG) is developed to produce the adjoint codes, which are used to analytically calculate gradients and the Hessian-vector products with the costs independent of the number of the independent variables. Different from other automatic differentiation tools, the implementation of ADG has advantages of using the least program behavior decomposition method and several static dependence analysis techniques. In this paper we first address the concerned concepts and fundamentals, and then introduce the functionality and the features of ADG. In particular, we also discuss the design architecture of ADG and implementation details including the recomputation and storing strategy and several techniques for code optimization. Some experimental results in several applications are presented at the end. Supported by the National Natural Science Foundation of China (Grant Nos. 60503031, 10871014), and the National Basic Research Program of China (Grant No. 2004CB418304)     

Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

Based on high order dynamic neural network, this paper presents the tracking problem for uncertain nonlinear composite system, which contains external disturbance, whose nonlinearities are assumed to be unknown. A smooth controller is designed to guarantee a uniform ultimate boundedness property for the tracking error and all other signals in the dosed loop. Certain measures are utilized to test its performance. No a priori knowledge of an upper bound on the “optimal” weight and modeling error is required; the weights of neural networks are updated on-line. Numerical simulations performed on a simple example illustrate and clarify the approach.     

Time-varying clustering for local lighting and material design

This paper presents an interactive graphics processing unit (GPU)-based relighting system in which local lighting condition, surface materials and viewing direction can all be changed on the fly. To support these changes, we simulate the lighting transportation process at run time, which is normally impractical for interactive use due to its huge computational burden. We greatly alleviate this burden by a hierarchical structure named a transportation tree that clusters similar emitting samples together within a perceptually acceptable error bound. Furthermore, by exploiting the coherence in time as well as in space, we incrementally adjust the clusters rather than computing them from scratch in each frame. With a pre-computed visibility map, we are able to efficiently estimate the indirect illumination in parallel on graphics hardware, by simply summing up the radiance shoots from cluster representatives, plus a small number of operations of merging and splitting on clusters. With relighting based on the time-varying clusters, interactive update of global illumination effects with multi-bounced indirect lighting is demonstrated in applications to material animation and scene decoration. Supported by the National Basic Research Program of China (Grant No. 2009CB320802), the National Natural Science Foundation of China (Grant No. 60833007), the National High-Tech Research & Development Progran of China (Grant No. 2008AA01Z301), and the Research Grant of the University of Macau     

Mixed frequency small gain state feedback control with application to insulin pumps

This paper considers the state feedback control synthesis problem for linear continuous-time systems with small gain specifications in mixed frequency ranges. A new method for designing the state feedback controllers is developed in the framework of linear matrix inequality (LMI) approach. Finally, the effectiveness of the proposed method in comparison with the available result is illustrated via an application to insulin pumps. Recommended by Editorial Board member Poo Gyeon Park under the direction of Editor Jae Weon Choi. This work was supported in part by the Funds for Creative Research Groups of China (No. 60821063), the State Key Program of National Natural Science of China (Grant No. 60534010), National 973 Program of China (Grant No. 2009CB320604), the Funds of National Science of China (Grant No. 60674021), the 111 Project (B08015) and the Funds of PhD program of MOE, China (Grant No. 20060145019). Xiao-Ni Zhang received the B.E. and M.S. degree from the Shenyang Normal University, China, in 1999 and 2003, respectively, and a Ph.D. candidate at Northeastern University. Her research interest covers robust control, mixed frequency optimal control and reliable control. Guang-Hong Yang received the B.S. and M.S. degrees in Northeast University of Technology, China, in 1983 and 1986, respectively, and the Ph.D. degree in Control Engineering from Northeastern University, China (formerly, Northeast University of Technology), in 1994. He was a Lecturer/Associate Professor with Northeastern University from 1986 to 1995. He joined the Nanyang Technological University in 1996 as a Postdoctoral Fellow. From 2001 to 2005, he was a Research Scientist/Senior Research Scientist with the National University of Singapore. He is currently a Professor at the College of Information Science and Engineering, Northeastern University. His current research interests include fault-tolerant control, fault detection and isolation, nonfragile control systems design, and robust control. Dr. Yang is an Associate Editor for the International Journal of Control, Automation, and Systems (IJCAS), and an Associate Editor of the Conference Editorial Board of the IEEE Control Systems Society.     

Forward/backward prediction solution for adaptive noisy FIR filtering

An important and hard problem in signal processing is the estimation of parameters in the presence of observation noise. In this paper, adaptive finite impulse response (FIR) filtering with noisy input-output data is considered and two developed bias compensation least squares (BCLS) methods are proposed. By introducing two auxiliary estimators, the forward output predictor and the backward output predictor are constructed respectively. By exploiting the statistical properties of the cross-correlation function between the least squares (LS) error and the forward/backward prediction error, the estimate of the input noise variance is obtained; the effect of the bias can thereafter be removed. Simulation results are presented to illustrate the good performances of the proposed algorithms. Supported by the National Natural Science Foundation of China for Distinguished Young Scholars (Grant No. 60625104), the Ministerial Foundation of China (Grant No. A2220060039) and the Fundamental Research Foundation of BIT (Grant No. 1010050320810)