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In recent years, research on the complex networks vulnerability attracts the attention of the academic circles and engineering institutes, and a lot of high value analysis models have been put forward which had solved many theoretical and practical problems with many cascading failures occurring frequently in practical large-scale networks. Apparently, for the complex network vulnerability assessment...
This paper presents the algorithm and software developed for parallel simulation of spiking neural networks on multiple SpiNNaker universal neuromorphic chips. It not only describes approaches to simulating neural network models, such as dynamics, neural representations, and synaptic delays, but also presents the software design of loading a neural application and initial a simulation on the multi-chip...
A new type of breakout prediction system based on multilevel neural network for continuous casting was proposed, which consists of a pattern recognition unit of single-thermocouple temperature pattern based on BP neural network, a logic judgment unit of multi-thermocouple temperature pattern and a decision making unit of fuzzy neural network based on T-S (Takagi-Sugeno) model. In the training of BP...
Symbolic time series analysis has been introduced in recent literature for pattern identification in dynamical systems. Relevant information, embedded in the measured time series, is extracted in the form of symbol sequences by partitioning of the data sets, and probabilistic finite state automata are constructed from these symbol sequences to generate pattern vectors. This paper presents a symbolic...
Configuring a million-core parallel system at boot time is a difficult process when the system has neither specialised hardware support for the configuration process nor a preconfigured default state that puts it in operating condition. SpiNNaker is a parallel Chip Multiprocessor (CMP) system for neural network (NN) simulation. Where most large CMP systems feature a sideband network to complete the...
We propose a system based on the Izhikevich model running on a scalable chip multiprocessor - SpiNNaker - for large-scale spiking neural network simulation. The design takes into account the requirements for processing, storage, and communication which are essential to the efficient modelling of spiking neural networks. To gain a speedup of the processing as well as saving storage space, the Izhikevich...
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