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

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 69-71). Also available via the Internet.

Thesis (M. S.)--Industrial Design, Georgia Institute of Technology, 2007. / Randy Bernard, Committee Member ; Abir Mullick, Committee Member ; Wayne Chung, Committee Chair.

Adaptive FeedforWard algorithms have been successfully applied in the active control of sound and vibrations~ Current approaches, such as the Filtered-X Least Mean Square algorithm, are limited in adaptability and applicability by the computationally intensive system identification process. The Time Average approach is a technique that promises to be a major advance in the active control of sound and vibration. This approach is based on statistical estimates of the error signals, and does not require having previous knowledge of the system. A new minimization algorithm has been developed, which enhances the performance of the Time Average controllers. A new technique to estimate the cost function is also presented. This technique is called the Real Time Cost Function Evaluation method. Based upon these new controller design concepts, single channel and multi-channel controllers were developed and experimentally tested. The experiments included control of a harmonically excited, simply supported beam, and the control of inlet tonal noise radiated from a JT1SD-l turbofan engine. / Master of Science

by Hau-san Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [178-183]). / Chapter 1 --- INTRODUCTION / Chapter 1.1 --- Learning versus Explicit Programming --- p.1-1 / Chapter 1.2 --- Artificial Neural Networks --- p.1-2 / Chapter 1.3 --- Learning in ANN --- p.1-3 / Chapter 1.4 --- Problems of Learning in BP Networks --- p.1-5 / Chapter 1.5 --- Dynamic Node Architecture for BP Networks --- p.1-7 / Chapter 1.6 --- Incremental Learning --- p.1-10 / Chapter 1.7 --- Research Objective and Thesis Organization --- p.1-11 / Chapter 2 --- THE FEEDFORWARD MULTILAYER NEURAL NETWORK / Chapter 2.1 --- The Perceptron --- p.2-1 / Chapter 2.2 --- The Generalization of the Perceptron --- p.2-4 / Chapter 2.3 --- The Multilayer Feedforward Network --- p.2-5 / Chapter 3 --- SOLUTIONS TO THE BP LEARNING PROBLEM / Chapter 3.1 --- Introduction --- p.3-1 / Chapter 3.2 --- Attempts in the Establishment of a Viable Hidden Representation Model --- p.3-5 / Chapter 3.3 --- Dynamic Node Creation Algorithms --- p.3-9 / Chapter 3.4 --- Concluding Remarks --- p.3-15 / Chapter 4 --- THE GROWTH ALGORITHM FOR NEURAL NETWORKS / Chapter 4.1 --- Introduction --- p.4-2 / Chapter 4.2 --- The Radial Basis Function --- p.4-6 / Chapter 4.3 --- The Additional Input Node and the Modified Nonlinearity --- p.4-9 / Chapter 4.4 --- The Initialization of the New Hidden Node --- p.4-11 / Chapter 4.5 --- Initialization of the First Node --- p.4-15 / Chapter 4.6 --- Practical Considerations for the Growth Algorithm --- p.4-18 / Chapter 4.7 --- The Convergence Proof for the Growth Algorithm --- p.4-20 / Chapter 4.8 --- The Flow of the Growth Algorithm --- p.4-21 / Chapter 4.9 --- Experimental Results and Performance Analysis --- p.4-21 / Chapter 4.10 --- Concluding Remarks --- p.4-33 / Chapter 5 --- KNOWLEDGE REPRESENTATION IN NEURAL NETWORKS / Chapter 5.1 --- An Alternative Perspective to Knowledge Representation in Neural Network: The Temporal Vector (T-Vector) Approach --- p.5-1 / Chapter 5.2 --- Prior Research Works in the T-Vector Approach --- p.5-2 / Chapter 5.3 --- Formulation of the T-Vector Approach --- p.5-3 / Chapter 5.4 --- Relation of the Hidden T-Vectors to the Output T-Vectors --- p.5-6 / Chapter 5.5 --- Relation of the Hidden T-Vectors to the Input T-Vectors --- p.5-10 / Chapter 5.6 --- An Inspiration for a New Training Algorithm from the Current Model --- p.5-12 / Chapter 6 --- THE DETERMINISTIC TRAINING ALGORITHM FOR NEURAL NETWORKS / Chapter 6.1 --- Introduction --- p.6-1 / Chapter 6.2 --- The Linear Independency Requirement for the Hidden T-Vectors --- p.6-3 / Chapter 6.3 --- Inspiration of the Current Work from the Barmann T-Vector Model --- p.6-5 / Chapter 6.4 --- General Framework of Dynamic Node Creation Algorithm --- p.6-10 / Chapter 6.5 --- The Deterministic Initialization Scheme for the New Hidden Nodes / Chapter 6.5.1 --- Introduction --- p.6-12 / Chapter 6.5.2 --- Determination of the Target T-Vector / Chapter 6.5.2.1 --- Introduction --- p.6-15 / Chapter 6.5.2.2 --- Modelling of the Target Vector βQhQ --- p.6-16 / Chapter 6.5.2.3 --- Near-Linearity Condition for the Sigmoid Function --- p.6-18 / Chapter 6.5.3 --- Preparation for the BP Fine-Tuning Process --- p.6-24 / Chapter 6.5.4 --- Determination of the Target Hidden T-Vector --- p.6-28 / Chapter 6.5.5 --- Determination of the Hidden Weights --- p.6-29 / Chapter 6.5.6 --- Determination of the Output Weights --- p.6-30 / Chapter 6.6 --- Linear Independency Assurance for the New Hidden T-Vector --- p.6-30 / Chapter 6.7 --- Extension to the Multi-Output Case --- p.6-32 / Chapter 6.8 --- Convergence Proof for the Deterministic Algorithm --- p.6-35 / Chapter 6.9 --- The Flow of the Deterministic Dynamic Node Creation Algorithm --- p.6-36 / Chapter 6.10 --- Experimental Results and Performance Analysis --- p.6-36 / Chapter 6.11 --- Concluding Remarks --- p.6-50 / Chapter 7 --- THE GENERALIZATION MEASURE MONITORING SCHEME / Chapter 7.1 --- The Problem of Generalization for Neural Networks --- p.7-1 / Chapter 7.2 --- Prior Attempts in Solving the Generalization Problem --- p.7-2 / Chapter 7.3 --- The Generalization Measure --- p.7-4 / Chapter 7.4 --- The Adoption of the Generalization Measure to the Deterministic Algorithm --- p.7-5 / Chapter 7.5 --- Monitoring of the Generalization Measure --- p.7-6 / Chapter 7.6 --- Correspondence between the Generalization Measure and the Generalization Capability of the Network --- p.7-8 / Chapter 7.7 --- Experimental Results and Performance Analysis --- p.7-12 / Chapter 7.8 --- Concluding Remarks --- p.7-16 / Chapter 8 --- THE ESTIMATION OF THE INITIAL HIDDEN LAYER SIZE / Chapter 8.1 --- The Need for an Initial Hidden Layer Size Estimation --- p.8-1 / Chapter 8.2 --- The Initial Hidden Layer Estimation Scheme --- p.8-2 / Chapter 8.3 --- The Extension of the Estimation Procedure to the Multi-Output Network --- p.8-6 / Chapter 8.4 --- Experimental Results and Performance Analysis --- p.8-6 / Chapter 8.5 --- Concluding Remarks --- p.8-16 / Chapter 9 --- CONCLUSION / Chapter 9.1 --- Contributions --- p.9-1 / Chapter 9.2 --- Suggestions for Further Research --- p.9-3 / REFERENCES --- p.R-1 / APPENDIX --- p.A-1

Lin Pui-Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 103-105). / Abstracts in English and Chinese. / Acknowledgement / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Nonlinear Phenomenon of Power Amplifier --- p.5 / Chapter 2.1. --- AM-AM and AM-PM Distortion --- p.5 / Chapter 2.2. --- Intermodulation Distortion --- p.7 / Chapter Chapter 3 --- Linearization Techniques --- p.10 / Chapter 3.1. --- Power Backoff --- p.11 / Chapter 3.2. --- Feedback --- p.12 / Chapter 3.3. --- Predistortion --- p.12 / Chapter 3.4. --- Feedforward --- p.14 / Chapter 3.5. --- Other Linearization Techniques --- p.15 / Chapter Chapter 4 --- Analysis of Feedforward Power Amplifier --- p.17 / Chapter 4.1. --- Feedforward Efficiency --- p.18 / Chapter 4.2. --- Design Criteria of the Auxiliary Amplifier --- p.20 / Chapter 4.3. --- Sensitivity Analysis --- p.21 / Chapter 4.3.1. --- Phase and Amplitude Mismatch --- p.22 / Chapter 4.3.2. --- Delay Mismatch --- p.23 / Chapter 4.3.3. --- Combined Effect --- p.25 / Chapter 4.3.4. --- Practical Consideration --- p.27 / Chapter 4.4. --- Other Design Criteria --- p.28 / Chapter Chapter 5 --- Adaptive Control Networks for FFPA --- p.29 / Chapter 5.1. --- Basic Principles of the Adaptive Control Network --- p.30 / Chapter 5.1.1. --- Lookup Table --- p.30 / Chapter 5.1.2. --- Power Minimization Vs. Correlation --- p.31 / Chapter 5.2. --- Analog Vs Digital Implementation of the Adaptive Control Network --- p.34 / Chapter 5.3. --- Techniques for Improving the Convergence Behaviour at the Distortion Cancellation Loop --- p.35 / Chapter 5.4. --- Important Notes on the Control Networks --- p.38 / Chapter Chapter 6 --- Novel Analysis of Adaptive FFPA --- p.40 / Chapter 6.1. --- Gradient algorithm --- p.40 / Chapter 6.2. --- Dual Loop Adaptive FFPA --- p.41 / Chapter 6.2.1. --- System Modeling --- p.42 / Chapter 6.2.2. --- Adaptation Behavior of the Distortion Extraction Loop --- p.44 / Chapter 6.2.3. --- Adaptation Behavior of the Distortion Cancellation Loop --- p.48 / Chapter 6.2.4. --- Accuracy Requirement of the Control Signals --- p.50 / Chapter 6.2.5. --- Effect of Delay Mismatch on the Convergence Accuracy --- p.51 / Chapter 6.2.6. --- Convergence Behaviors for Two Tone Input Signal --- p.52 / Chapter 6.2.6.1. --- Distortion Extraction Loop --- p.53 / Chapter 6.2.6.2. --- Distortion Cancellation Loop --- p.55 / Chapter 6.2.6.3. --- Simulation Results --- p.57 / Chapter 6.2.7. --- Convergence Behaviors for Digital Modulated Test signal --- p.60 / Chapter 6.2.7.1. --- Distortion Extraction Loop --- p.61 / Chapter 6.2.7.2. --- Distortion Cancellation Loop --- p.66 / Chapter 6.2.7.3. --- Simulation Results --- p.68 / Chapter 6.2.8. --- Comparison for the Adaptation Performance with Two Tone and Digital Modulated Test Signal --- p.70 / Chapter 6.3. --- Triple Loop Adaptive FFPA --- p.71 / Chapter 6.3.1. --- Adaptation Performance of the Additional Loop --- p.73 / Chapter 6.3.2. --- Adaptation Performance of the Distortion Cancellation Loop --- p.75 / Chapter 6.3.3. --- Improvement in Bias Error at the Distortion Cancellation Loop --- p.76 / Chapter 6.3.4. --- Effect of Delay Mismatch --- p.77 / Chapter 6.3.5. --- Simulation Results --- p.79 / Chapter Chapter 7 --- Implementation and Measured Performance of Triple Loop Adaptive FFPA --- p.85 / Chapter 7.1. --- Hardware Design --- p.85 / Chapter 7.1.1. --- Vector Modulator --- p.87 / Chapter 7.1.2 --- Complex Correlator --- p.88 / Chapter 7.2. --- Experimental Setup and Measured Results --- p.90 / Chapter Chapter 8 --- Conclusion --- p.95 / Appendix I Matlab Program for Simulation of Dual Loop Adaptive FFPA --- p.97 / Appendix II Matlab Program for Simulation of Triple Loop Adaptive FFPA --- p.100 / Reference --- p.103 / Author's Publication --- p.106

Critical steps in the design process is the gathering of data, processing the data into a useful form of information (a design concept) which meets specific needs, passing this refined design solution down the path to production, where it is released into the larger environment. With in the designing process there are multiple feedback loops as the solution becomes more refined. Even as it reaches the final end user, other design refinement feedback loops continue as new and improved products or systems. Along with the interdisciplinary teams involved with the product/system development, the more complexity the product or system becomes the more critical the organization of the data becomes. This paper will present and test a concept of a design feedback and feed forward communication tool for product/system design that uses Dr. Walter A. Schaer s Three Functions of an Artifact as the methodological structure for design development. The essence of this design tool is the merging of a new communication system within an existing methodology of organizing complex systems into a morphological matrix, developed by Dr. Walter A. Schaer, based on the Charles Morris s work on semiotics. This communication tool is a new feedback / feed forward mechanism which correspond with the semiotic structure in a morphological matrix to assist the designer develop design solutions. The research will measure the success rate of the tool in the design process, examine of how the designers took advantage of the new tool, and evaluate their perception of its usefulness.

Bibliography: leaves 176-188 / xvii, 188 leaves : ill ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1992?

Thesis (Ph. D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1992? / Includes bibliographical references (leaves 176-188).

Thesis (Ph. D.)--University of California, San Diego, 1997. / Vita. Includes bibliographical references.