The problem of designing recurrent continuous-time and spiking neural networks is NP-Hard. A common practice is to utilize stochastic searches, such as evolutionary algorithms, to automatically construct acceptable ne...
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The problem of designing recurrent continuous-time and spiking neural networks is NP-Hard. A common practice is to utilize stochastic searches, such as evolutionary algorithms, to automatically construct acceptable networks. The outcome of the stochastic search is related to its ability to navigate the search space of neural networks and discover those of high quality. In this paper we investigate the search space associated with designing the above recurrent neural networks in order to differentiate which network should be easier to automatically design via a stochastic search. Our investigation utilizes two popular dynamic systems problems; (1) the Henon map and (2) the inverted pendulum as a benchmark.
A cellular neural/nonlinear network (CNN) is any spatial arrangement of mainly locally coupled cells, where each cell is a dynamical system which has an input, an output and a state that evolves according to some pres...
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A cellular neural/nonlinear network (CNN) is any spatial arrangement of mainly locally coupled cells, where each cell is a dynamical system which has an input, an output and a state that evolves according to some prescribed dynamical laws. Since the CNN was first introduced in 1988, research in this field has developed rapidly. The goal of this tutorial is to provide participants with a snapshot of the current state of the art and research trends. It covers the broad multi-disciplinary areas of CNN research, from theoretical aspects to applications. The presenters have experience ranging from theoretical analysis of CNN dynamics, VLSI implementation and system level applications
We propose adaptive nonlinear auto-associative modeling (ANAM) based on Locally Linear Embedding algorithm (LLE) for learning intrinsic principal features of each concept separately and recognition thereby. Unlike tra...
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This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines ...
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This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM). Within the STL framework, many conventional learning machines can be generalized to take n/sup th/-order tensors as inputs. We also study the applications of tensors to learning machine design and feature extraction by linear discriminant analysis (LDA). Our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (TR1DA). These generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machine learning and data mining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. As an example, we generalize MPM to its STL version, which is named the tensor MPM (TMPM). TMPM learns a series of tensor projections iteratively. It is then evaluated against the original MPM. Our experiments on a binary classification problem show that TMPM significantly outperforms the original MPM.
A new interactive floorspace has been developed which uses modular nodes connected together to create a pressure-sensitive area of varying size and shape, giving it the potential to be integrated into an interactive e...
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Instead of making decisions with fast but local dispatching functions, the increasing computing power and up-to-moment information provisioning have made possible real-time vehicle scheduling in an automated material ...
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Instead of making decisions with fast but local dispatching functions, the increasing computing power and up-to-moment information provisioning have made possible real-time vehicle scheduling in an automated material handling environment in 300mm semiconductor manufacturing. To our best knowledge, this paper is the first work to adopt the scheduling approach to solve the 300mm vehicle transportation problems. We adopt Petri nets to model the coupling dynamics among transport jobs and OHT vehicles in an intrabay loop. The congestion phenomenon among OHT vehicles is captured. The OHT scheduling problem is then formulated into an integer programming problem whose goal is to efficiently allocate OHT vehicles to jobs such that average job delivery time is minimized. A solution methodology that combines Lagrangian relaxation and the surrogate subgradient methods [13] is developed. To reduce computational efforts in solving each subproblem optimally, an approximation method is developed to solve for subproblems. A heuristic algorithm is developed to adjust the dual solution to a feasible schedule. Numerical results demonstrate that our solution methodology can generate good schedules within a reasonable amount of computation time for realistic problems. Compared to a popular vehicle dispatching rule, our approach can achieve 25.6% improvements on the average delivery time in our realistic test cases.
A hierarchical approach for the abstraction of digital VLSICs is presented. Circuit layout is hierarchically abstracted into logical constructs of binary tree structures, which may be manipulated to extract circuit fu...
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A hierarchical approach for the abstraction of digital VLSICs is presented. Circuit layout is hierarchically abstracted into logical constructs of binary tree structures, which may be manipulated to extract circuit functionality for the purpose of verifying design correctness. VLSIC design specification in the form of HDL is hierarchically decomposed to generate logical formulae for the given specification. By comparing the above, a verification report is obtained
A method of image sequence encoding is presented, based on fractal compression of 3D data blocks without searching. A simple fractal transform scheme is used that only depends on one real-valued coefficient. We have d...
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ISBN:
(纸本)9539676940
A method of image sequence encoding is presented, based on fractal compression of 3D data blocks without searching. A simple fractal transform scheme is used that only depends on one real-valued coefficient. We have developed a software codec for the Windows95 operating system, that carries out video streams in real-time. The method can be further developed for application to videoconferencing, videotelephony or streaming video over the Internet.
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