Insight in problem solving : developing a neural network theoretical account of the processes involved in attaining insight

Bibliography: leaves 151-164.Insight has enjoyed the reputation of an elusive phenomenon in psychology and insight problems are very difficult to solve. Only very specific hints concerning their solution have been found to significantly increase the number of problem solvers who are able to solve insight problems. The result of this has been to suggest that insight does not exist, that it is a mysterious phenomenon, or that it is an aspect of problem solving which we have so far failed to understand. Insight in problem solving is investigated from the perspective that the phenomenon needs explanation and it is argued that, while insight has been operationally defined and a clear set of key empirical findings have been established, the conceptual explanation of insight has been largely ignored. It is suggested that a conceptual account of insight is needed so that this aspect of cognitive processing can be incorporated into the main body of cognitive research on problem solving. The current tension in cognitive science and cognitive psychology is examined and it is argued that writing a conceptual account of insight in neural network theoretical terms will not only advance our understanding of insight, but will also reflect on the debate in cognitive theory. This is a result of its status as an aspect of problem solving and as a phenomenon which symbolic theory has so far failed to offer a clear explanation for. A conceptual account of insight in neural network terms is advanced which offers a comprehensive account of the key empirical findings on insight. It is suggested that insight can be understood as the recognition of a pattern to insight problems. Predictions derived from the theory suggest that overcoming the effects of past learning, employing conceptual transfer, and fostering expertise at insight problem solving will significantly facilitate insightful problem solution

Recurrent neural network for optimization with application to computer vision.

by Cheung Kwok-wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves [146-154]).Chapter Chapter 1 --- IntroductionChapter 1.1 --- Programmed computing vs. neurocomputing --- p.1-1Chapter 1.2 --- Development of neural networks - feedforward and feedback models --- p.1-2Chapter 1.3 --- State of art of applying recurrent neural network towards computer vision problem --- p.1-3Chapter 1.4 --- Objective of the Research --- p.1-6Chapter 1.5 --- Plan of the thesis --- p.1-7Chapter Chapter 2 --- BackgroundChapter 2.1 --- Short history on development of Hopfield-like neural network --- p.2-1Chapter 2.2 --- Hopfield network model --- p.2-3Chapter 2.2.1 --- Neuron's transfer function --- p.2-3Chapter 2.2.2 --- Updating sequence --- p.2-6Chapter 2.3 --- Hopfield energy function and network convergence properties --- p.2-1Chapter 2.4 --- Generalized Hopfield network --- p.2-13Chapter 2.4.1 --- Network order and generalized Hopfield network --- p.2-13Chapter 2.4.2 --- Associated energy function and network convergence property --- p.2-13Chapter 2.4.3 --- Hardware implementation consideration --- p.2-15Chapter Chapter 3 --- Recurrent neural network for optimizationChapter 3.1 --- Mapping to Neural Network formulation --- p.3-1Chapter 3.2 --- Network stability verse Self-reinforcement --- p.3-5Chapter 3.2.1 --- Quadratic problem and Hopfield network --- p.3-6Chapter 3.2.2 --- Higher-order case and reshaping strategy --- p.3-8Chapter 3.2.3 --- Numerical Example --- p.3-10Chapter 3.3 --- Local minimum limitation and existing solutions in the literature --- p.3-12Chapter 3.3.1 --- Simulated Annealing --- p.3-13Chapter 3.3.2 --- Mean Field Annealing --- p.3-15Chapter 3.3.3 --- Adaptively changing neural network --- p.3-16Chapter 3.3.4 --- Correcting Current Method --- p.3-16Chapter 3.4 --- Conclusions --- p.3-17Chapter Chapter 4 --- A Novel Neural Network for Global Optimization - Tunneling NetworkChapter 4.1 --- Tunneling Algorithm --- p.4-1Chapter 4.1.1 --- Description of Tunneling Algorithm --- p.4-1Chapter 4.1.2 --- Tunneling Phase --- p.4-2Chapter 4.2 --- A Neural Network with tunneling capability Tunneling network --- p.4-8Chapter 4.2.1 --- Network Specifications --- p.4-8Chapter 4.2.2 --- Tunneling function for Hopfield network and the corresponding updating rule --- p.4-9Chapter 4.3 --- Tunneling network stability and global convergence property --- p.4-12Chapter 4.3.1 --- Tunneling network stability --- p.4-12Chapter 4.3.2 --- Global convergence property --- p.4-15Chapter 4.3.2.1 --- Markov chain model for Hopfield network --- p.4-15Chapter 4.3.2.2 --- Classification of the Hopfield markov chain --- p.4-16Chapter 4.3.2.3 --- Markov chain model for tunneling network and its convergence towards global minimum --- p.4-18Chapter 4.3.3 --- Variation of pole strength and its effect --- p.4-20Chapter 4.3.3.1 --- Energy Profile analysis --- p.4-21Chapter 4.3.3.2 --- Size of attractive basin and pole strength required --- p.4-24Chapter 4.3.3.3 --- A new type of pole eases the implementation problem --- p.4-30Chapter 4.4 --- Simulation Results and Performance comparison --- p.4-31Chapter 4.4.1 --- Simulation Experiments --- p.4-32Chapter 4.4.2 --- Simulation Results and Discussions --- p.4-37Chapter 4.4.2.1 --- Comparisons on optimal path obtained and the convergence rate --- p.4-37Chapter 4.4.2.2 --- On decomposition of Tunneling network --- p.4-38Chapter 4.5 --- Suggested hardware implementation of Tunneling network --- p.4-48Chapter 4.5.1 --- Tunneling network hardware implementation --- p.4-48Chapter 4.5.2 --- Alternative implementation theory --- p.4-52Chapter 4.6 --- Conclusions --- p.4-54Chapter Chapter 5 --- Recurrent Neural Network for Gaussian FilteringChapter 5.1 --- Introduction --- p.5-1Chapter 5.1.1 --- Silicon Retina --- p.5-3Chapter 5.1.2 --- An Active Resistor Network for Gaussian Filtering of Image --- p.5-5Chapter 5.1.3 --- Motivations of using recurrent neural network --- p.5-7Chapter 5.1.4 --- Difference between the active resistor network model and recurrent neural network model for gaussian filtering --- p.5-8Chapter 5.2 --- From Problem formulation to Neural Network formulation --- p.5-9Chapter 5.2.1 --- One Dimensional Case --- p.5-9Chapter 5.2.2 --- Two Dimensional Case --- p.5-13Chapter 5.3 --- Simulation Results and Discussions --- p.5-14Chapter 5.3.1 --- Spatial impulse response of the 1-D network --- p.5-14Chapter 5.3.2 --- Filtering property of the 1-D network --- p.5-14Chapter 5.3.3 --- Spatial impulse response of the 2-D network and some filtering results --- p.5-15Chapter 5.4 --- Conclusions --- p.5-16Chapter Chapter 6 --- Recurrent Neural Network for Boundary DetectionChapter 6.1 --- Introduction --- p.6-1Chapter 6.2 --- From Problem formulation to Neural Network formulation --- p.6-3Chapter 6.2.1 --- Problem Formulation --- p.6-3Chapter 6.2.2 --- Recurrent Neural Network Model used --- p.6-4Chapter 6.2.3 --- Neural Network formulation --- p.6-5Chapter 6.3 --- Simulation Results and Discussions --- p.6-7Chapter 6.3.1 --- Feasibility study and Performance comparison --- p.6-7Chapter 6.3.2 --- Smoothing and Boundary Detection --- p.6-9Chapter 6.3.3 --- Convergence improvement by network decomposition --- p.6-10Chapter 6.3.4 --- Hardware implementation consideration --- p.6-10Chapter 6.4 --- Conclusions --- p.6-11Chapter Chapter 7 --- Conclusions and Future ResearchesChapter 7.1 --- Contributions and Conclusions --- p.7-1Chapter 7.2 --- Limitations and Suggested Future Researches --- p.7-3References --- p.R-lAppendix I The assignment of the boundary connection of 2-D recurrent neural network for gaussian filtering --- p.Al-1Appendix II Formula for connection weight assignment of 2-D recurrent neural network for gaussian filtering and the proof on symmetric property --- p.A2-1Appendix III Details on reshaping strategy --- p.A3-

Traffic management and control of automated guided vehicles using artificial neural networks

An industrial traffic management and control system based on Automated Guided Vehicles faces several combined problems. Decisions must be made concerning which vehicles will respond, or are allocated to each of the transport orders. Once a vehicle is allocated a transport order, a route has to be selected that allows it to reach its target location. In order for the vehicle to move efficiently along the selected route it must be provided with the means to recognise and adapt to the changing characteristics of the path it must follow. When several vehicles are involved these decisions are interrelated and must take into account the coordination of the movements of the vehicles in order to avoid collisions and maximise the performance of the transport system. This research concentrates on the problem of routing the vehicles that have already been assigned destinations associated with transport orders. In nearly all existing AGV systems this problem is simplified by considering there to be a fixed route between source and destination workstations. However if the system is to be used more efficiently, and particularly if it must support the requirements of modern manufacturing strategies, such as Justin- Time and Flexible Manufacturing Systems, of moving very small batches more frequently, then there is a need for a system capable of dealing with the increased complexity of the routing problem. The consideration of alternative paths between any two workstations together with the possibility of other vehicles blocking routes while waiting at a particular location, increases enormously the number of alternatives that must be considered in order to identify the routes for each vehicle leading to an optimum solution. Current methods used to solve this type of problem do not provide satisfactory solutions for all cases, which leaves scope for improvement. The approach proposed in this work takes advantage of the use of Backpropagation Artificial Neural Networks to develop a solution for the routing problem. A novel aspect of the approach implemented is the use of a solution derived for routing a single vehicle in a physical layout when some pieces of track are set as unavailable, as the basis for the solution when several vehicles are involved. Another original aspect is the method developed to deal with the problem of selecting a route between two locations based on an analysis of the conditions of the traffic system, when each movement decision has to be made. This lead to the implementation of a step-by-step search of the available routes for each vehicle. Two distinct phases can be identified in the approach proposed. First the design of a solution based on an ANN to solve the single vehicle case, and subsequently the development and testing of a solution for a multi-vehicle case. To test and implement these phases a specific layout was selected, and an algorithm was implemented to generate the data required for the design of the ANN solution. During the development of alternative solutions it was found that the addition of simple rules provided a useful means to overcome some of the limitations of the ANN solution, and a "hybrid" solution was originated. Numerous computer simulations were performed to test the solutions developed against alternatives based on the best published heuristic rules. The results showed that while it was not possible to generate a globally optimal solution, near optimal solutions could be obtained and the best hybrid solution was marginally better than the best of the currently available heuristic rules

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering