The organization I work in has 13 subsidiary businesses operating in the branded footwear and apparel industry. The industry currently faces significant macroeconomic and industry challenges. One of our biggest challenges is how to avoid excessive and wasteful new product development whilst still building an attractive range of products for the customer. So the focus of my research is on the management control and governance of the New Product Development (NPD) process to solve a pressing business problem. However, there is a gap in the literature. Many authors have claimed that our knowledge of the governance of NPD processes is incomplete and there is a dearth of actual studies in this area. My literature review looked at management control and in particular at the enduring problem of the need to generate control without stifling creativity. The literature led me to focus on the use of feedforward controls to influence NPD management teams to improve portfolio value and strategic alignment whilst simultaneously encouraging NPD experimentation. During this research I developed the concept of Feedforward Anticipatory Control (FAC), which encompasses the combination of feedforward control and double-loop learning. From this start my research question became “How does the use of FAC influence NPD management teams to improve portfolio value and strategic alignment?” From theory and my initial case study research I developed, tested and refined a tool for ascertaining the level of FAC sophistication in use by NPD teams in their development process. The tool was then used in action research interventions to help the teams develop their sophistication in the use of FAC. The tool was found to be useable, useful and have value. The action research case studies were embedded in a case study protocol to ensure the rigour of my research. This involved developing a framework to investigate the consequences of my interventions, in terms of both hard performance metrics and softer team perceptions. The contribution is in the use of management controls in NPD. The findings show that different levels of FAC sophistication can be applied in NPD and that the use of higher levels of FAC influences NPD teams to improve portfolio value and strategic alignment. The contribution to practice is an intervention “toolkit” that can influence NPD teams to develop higher levels of FAC sophistication and generate improvements in NPD portfolio performance.

Ng Chi-fai. / Thesis submitted in: December 2000. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 69-73). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / List of Figures --- p.viii / List of Tables --- p.ix / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Principles of Feedforward Neural Network Approximation --- p.1 / Chapter 1.3 --- Contribution of The Thesis --- p.5 / Chapter 1.4 --- Outline of The Thesis --- p.5 / Chapter 2 --- Feedforward Neural Networks: An Approximator for Nonlinear Control Law --- p.8 / Chapter 2.1 --- Optimization Methods Applied in Feedforward Neural Network Approximation --- p.8 / Chapter 2.2 --- Example in Supervised Learning --- p.10 / Chapter 2.2.1 --- Problem Description --- p.10 / Chapter 2.2.2 --- Neural Network Configuration and Training --- p.12 / Chapter 2.2.3 --- Simulation Result --- p.13 / Chapter 3 --- Neural Based Approximation of Center Manifold Equations --- p.19 / Chapter 3.1 --- Solving Center Manifold Equations by Feedforward Neural Network Approx- imation --- p.19 / Chapter 3.2 --- Example --- p.21 / Chapter 3.2.1 --- Problem Description --- p.21 / Chapter 3.2.2 --- Simulation Result --- p.24 / Chapter 3.2.3 --- Discussion --- p.24 / Chapter 4 --- Connection of Center Manifold Equations to Output Regulation Problem --- p.29 / Chapter 4.1 --- Output Regulation Theory --- p.29 / Chapter 4.2 --- Reduction of Regulator Equation into Center Manifold Equations --- p.31 / Chapter 5 --- Application to the Control Design of Ball and Beam System --- p.34 / Chapter 5.1 --- Problem Description --- p.34 / Chapter 5.2 --- Neural Approximation Solution of Center Manifold Equations --- p.37 / Chapter 5.3 --- Simulation Results --- p.38 / Chapter 5.4 --- Discussion --- p.45 / Chapter 6 --- Neural Based Disturbance Rejection of Nonlinear Benchmark Problem (TORA System) --- p.48 / Chapter 6.1 --- Problem Description --- p.48 / Chapter 6.2 --- Neural based Approximation of the Center Manifold Equations of TORA System --- p.51 / Chapter 6.3 --- Simulation Results --- p.53 / Chapter 6.4 --- Discussion --- p.59 / Chapter 7 --- Conclusion --- p.62 / Chapter 7.1 --- Future Works --- p.63 / Chapter A --- Center Manifold Theory --- p.64 / Chapter B --- Relation between Center Manifold Equation and Output Regulation Prob- lem --- p.66 / Biography --- p.68 / References --- p.69

by Jan C. Willems. / Bibliography: p. 11. / "August, 1981." / Supported in part by the U.S. Dept. of Energy under Contract DOE/ET-76-A-012295

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 95-99).

A hybrid operational amplifier compensation technique using Miller and multipath compensation is presented for multi-stage amplifier designs. Unconditional stability is achieved by the means of pole-zero cancellation where left-half zeros cancel out the non-dominant poles of the operational amplifier. The compensation technique is stable over process, temperature, and voltage variations. Compared to conventional Miller-compensation, the proposed compensation technique exhibits improved settling response for operational amplifiers with the same gain, bandwidth, power, and area. For the same settling time, the proposed compensation technique will require less area and consume less power than conventional Miller-compensation. Furthermore, the proposed technique exhibits improved output slew rate and lower noise over the conventional Miller-compensation technique. Two-stage operational amplifiers were designed in a 0.18µm CMOS process using the proposed technique and conventional Miller-compensated technique. The design procedure for the two-stage amplifier is applicable for higher-order amplifier designs. The amplifiers were incorporated into a switched-capacitor oscillator where the oscillation harmonics are dependent on the settling behaviour of the op amps. The superior settling response of the proposed compensation technique results in a improved output waveform from the oscillator.

Considering the requirements of the commercial and military applications on amplitude and phase linearity, it is necessary to reduce nonlinearity of the amplifiers. There are several linearization techniques that are used to reduce nonlinearity effects. Feedforward linearization technique is known as one of the best linearization methods due to its superior linearization performance and broadband operation. Vector modulators which allows amplitude and phase modulation simultaneously, is the most important component of a feedforward system. In this thesis, first of all a broadband V/UHF vector modulator designed and implemented. Then a feedforward system is investigated and implemented using the designed vector modulator for V/UHF band.

Recently, the accuracy of hot strip gage is strictly demanded, and the strip thickness becomes the most important quality in hot strip rolling. It is well known that the accuracy of final strip thickness in the hot strip mill depends on the gage control performance at each stand. In order to improve the quality of the hot strip gage and reduce the strip thickness deviation , the Automatic Gage Control(A.G.C) system now is widely used in modern hot strip mills of the world. In this paper, the principle of feedforward control strategy in the AGC system is discussed and it¡¦s control performance is deeply analyzed. Besides, based on the study of comprehensively mathematical model and the establishment of simulator, the mathematical analysis and the simulation result can clarify the influence of the control system on strip thickness.

<p>Detta arbete handlar om beteendeinriktad biometri och artificiella neuronnät av typen feedforward och hur de tillsammans kan användas för att verifiera användare. Det har av tidigare arbete bekräftats att det är möjligt att verifiera användare, men tidigare resultat har däremot inte utfört tester med avseende på avvikelser i data (beteende) och dess inverkan på verifieringen. Det är detta som utgör det huvudsakliga målet för detta arbete, nämligen att undersöka hur avvikelser i data påverkar verifiering och utifrån det också undersöka neuronnätens noggrannhet vid verifiering.</p>

We propose to develop new non-data aided (NDA) digital feedforward symbol timing estimators for linear and nonlinear modulations, with a view to reducing the sampling rate of the estimators. The proposed estimators rely on the fact that sufficient statistics exist for a signal sampled at the Nyquist rate. We propose an ad hoc extension to the timing estimator based on the log nonlinearity which performs better than existing estimators at this rate when the operating signal-to-noise ratio (SNR) and the excess bandwidth are low. We propose another alternative estimator for operating at the Nyquist rate that has reduced self-noise at high SNR for large rolloff factors. This can be viewed as an extension of the timing estimator based on the square law nonlinearity. For continuous phase modulations (CPM), we propose two novel estimators that can operate at the symbol rate for MSK type signals. Among the class of NDA feedforward timing estimators we are not aware of any other estimator that can function at symbol rate for this type of signals. We also propose several new estimators for the MSK modulation scheme which operate with reduced sampling rate and are robust to carrier frequency offset and phase offset.

Neural networks have become very important tools in many areas including economic researches. The objectives of this thesis are to examine the fundamental components, concepts and theory of neural network methods from econometric and statistic perspective, with particular focus on econometrically and statistically relevant models. In order to evaluate the relative effectiveness of econometric and neural network methods, two empirical studies are conducted by applying neural network methods in a methodological comparison fashion with traditional econometric models.Both neural networks and econometrics have similar models, common problems of modeling and interference. Neural networks and econometrics/statistics, particularly their discriminant methods, are two sides of the same coin in terms of the nature of modeling statistic issues. On one side, econometric models are sampling paradigm oriented methods, which estimate the distribution of the predictor variable separately for each class and combine these with the prior probabilities of each class occurring; while neural networks are one of the techniques based on diagnostic paradigm, which use theinformation from the samples to estimate the conditional probability of an observation belonging to each class, based on predictor variables. Hence, neural network and econometric/statistical methods (particularly, discriminant models) have the same properties, except that the natural parameterizations differ.The empirical studies indicate that neural network methods outperform or are as good as traditional econometric models including Multiple Regression Analysis, Linear Probability Model (LPM), and Logit model, in terms of minimizing the errors of in-sample predictions and out-of-sample forecasts. Although neural networks have some advantages over econometric methods, they have some limitations too. Hence, neural networks are perhaps best viewed as supplements to econometric methods in studying economic issues, and not necessarily as substitutes.