Phd thesis artificial neural network

Phd thesis artificial neural network

3,views. 3, downloads. Abstract. This thesis presents a method for solving partial differential equations (PDEs) using articial neural networks. The method uses a constrained backpropagation (CPROP) approach for preserving prior knowledge during incremental training for solving nonlinear elliptic and parabolic PDEs adaptively, in non-stationary environments Artificial Neural Networks: A Financial Tool As Applied in the Australian Market Ph.D. Thesis by Clarence Nyap Watt Tan Bachelor of Science in Electrical Engineering Computers (), University of Southern California, Los Angeles, California, USA Master of Science in Industrial and Systems Engineering () Artificial Neural Network Thesis Topics are recently explored for student’s interest on Artificial Neural Network. This is one of our preeminent services which have attracted many students and research scholars due to its ever-growing research scope. Artificial Neural Network (ANN) is a mathematical model that used to predict the system performance which is inspired by the function and structure of human biological neural networks Estimated Reading Time: 3 mins

Research Artificial Neural Network Thesis Topics (Ideas)

This thesis presents a method for solving partial differential equations PDEs using articial neural networks. The method uses a constrained backpropagation CPROP approach for preserving prior knowledge during incremental training for solving nonlinear elliptic and parabolic PDEs adaptively, in non-stationary environments.

Compared to previous methods that use penalty functions or Lagrange multipliers. CPROP reduces the dimensionality of the optimization problem by using direct elimination, while satisfying the equality constraints associated with the boundary and initial conditions exactly, at every iteration of the algorithm.

The effectiveness of this method is demonstrated through several examples, including nonlinear elliptic. and parabolic PDEs with changing parameters and non-homogeneous terms.

The computational complexity analysis shows that CPROP compares favorably to existing methods of solution, phd thesis artificial neural network, and that it leads to considerable computational savings when subject to non-stationary environments, phd thesis artificial neural network. The CPROP based approach is extended to a constrained integration CINT method for solving initial boundary value partial differential equations PDEs. The CINT method combines classical Galerkin methods with CPROP in order to constrain the ANN to approximately satisfy the boundary condition at each stage of integration.

The advantage of the CINT method is that it is readily applicable to PDEs in irregular domains and requires no special modification for domains with complex geometries. Furthermore, the CINT method provides a semi-analytical solution that is infinitely differentiable, phd thesis artificial neural network. The CINT method is demonstrated on two hyperbolic and one parabolic initial boundary value problems IBVPs.

These IBVPs are widely used and have known analytical solutions. When compared with Matlab's nite phd thesis artificial neural network FE method, the CINT method is shown to achieve significant improvements both in terms of computational time and accuracy.

The CINT method is applied to a distributed optimal control DOC problem of computing optimal state and control trajectories for a multiscale dynamical system comprised of many interacting dynamical systems, or agents. A generalized reduced gradient GRG approach is presented in which the agent dynamics are described by a small system of stochastic dierential equations SDEs. A set of optimality conditions is derived using calculus of variations, and used to compute the optimal macroscopic state and microscopic control laws.

An indirect GRG approach is used to solve the optimality conditions numerically for large systems of agents. By assuming a parametric control law obtained from the superposition of linear basis functions, the agent control laws can be determined via set-point regulation, such. that the macroscopic behavior of the agents phd thesis artificial neural network optimized over time, based on multiple, interactive navigation objectives, phd thesis artificial neural network.

Lastly, the CINT method is used to identify optimal phd thesis artificial neural network profiles in water limited ecosystems. Knowledge of root depths and distributions is phd thesis artificial neural network in order to accurately model and predict hydrological ecosystem dynamics.

Therefore, there is interest in accurately predicting distributions for various vegetation types, phd thesis artificial neural network, soils, and climates.

Numerical experiments were were performed that identify root profiles that maximize transpiration over a 10 year period across a transect of the Kalahari.

Storm types were varied to show the dependence of the optimal profile on storm frequency and intensity. It is shown that more deeply distributed roots are optimal for regions where. storms are more intense and less frequent, and shallower roots are advantageous in regions where storms are less intense and more frequent.

Rights for Collection: Duke Dissertations. Skip to main content. View Item DukeSpace Theses and Dissertations Duke Dissertations View Item. Toggle navigation. JavaScript is disabled for your browser. Some features of this site may not work without it. Solving Partial Differential Equations Using Artificial Neural Networks. Rudd, Keith. Repository Usage Stats.

Compared to previous methods that use penalty functions or Lagrange multipliers, CPROP reduces the dimensionality of the optimization problem by using direct elimination, while satisfying the equality constraints associated with the boundary and initial conditions exactly, at every iteration of the algorithm.

The effectiveness of this method is demonstrated through several examples, including nonlinear elliptic and parabolic PDEs with changing parameters and non-homogeneous terms.

By assuming a parametric control law obtained from the superposition of linear basis functions, the agent control laws can be determined via set-point regulation, such that the macroscopic behavior of the agents is optimized over time, based on multiple, interactive navigation objectives. It is shown that more deeply distributed roots are optimal for regions where storms are more intense and less frequent, and shallower roots are advantageous in regions where storms are less intense and more frequent.

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Dieuwke Hupkes: PhD defence on neural networks and natural language processing

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Ph.D. thesis - Matthias Scholz - Max Planck Institute of Molecular Plant Physiology

3,views. 3, downloads. Abstract. This thesis presents a method for solving partial differential equations (PDEs) using articial neural networks. The method uses a constrained backpropagation (CPROP) approach for preserving prior knowledge during incremental training for solving nonlinear elliptic and parabolic PDEs adaptively, in non-stationary environments Ph.D. thesis Advances in biotechnologies rapidly increase the number of molecules of a cell which can be observed simultaneously. PCA and ICA applied to an artificial data set. Standard auto-associative neural network [ pdf | gif | png | eps] The network output x is required to be equal to the input x. Illustrated is a [ Mar 26,  · ORCID. PhD Thesis: Local Propagation in Neural Network Learning by Architectural Constraints. 6 minute read. Published: March 26, Abstract. A crucial role for the success of the Artificial Neural Networks (ANN) processing scheme has