Operating environments contain both fixed and moving interferers that correspond to both stationary and time-varying spatial distributions of path distortion of stationary and transient fading and multipath delays that impede connectivity. Real-world examples include wireless local area networks (LANs) within buildings and radio beacons in an outdoor mobile radio environment.
The purpose of the positioning of the devices is formation of a distributed network, either in a mesh or hub-spoke topology, that achieves robust connectivity of the nodes. The optimal location of wireless transceivers or communicating sensor devices in an urban area and within large human-made structures is considered. Once trained, the VMENA subsystem is capable of processing at approximately 75,000 feedforward passes/second, resulting in over twofold computational throughput improvement relative to the ISAbus based neural network architecture.
Boson x starfield 2 software#
Training sets ranging in size from 50 to 2500 pixels are utilized to train the network, and the best result for the hardware-in-the-loop learning is found to be comparable to the best result of the software NN simulation. As a demonstration, the NN hardware system is applied to a computationally intensive map-data classification problem. This supervised learning algorithm allows the network architecture to dynamically evolve by adding hidden neurons while modulating their synaptic weights using standard gradient-descent backpropagation. An efficient hardware-in-the-loop cascade backpropagation (CBP) learning algorithm is implemented on the hardware. the number of neurons per input, hidden, and output layer and the number of layers) could be carved out from the set of neuron and synapse resources. Under software control, the system architecture could be flexibly reconfigured from feedback to feedforward and vice versa, and once selected, the NN topology (i.e. The NN board is built around cascadable VLSI NN chips (32 X 32 synapse chips and 32 X 32 neuron/synapse composite chips) for a total of 64 neurons and over 8 K synapses.
Boson x starfield 2 Pc#
The VMEbus interface is specifically chosen to overcome the limited bandwidth of the PC host computer industrial standard architecture (ISA) bus. The inherent parallelism of an analog VLSI NN embodiment enables a fully parallel and hence high speed and high-throughput hardware implementation of NN architectures. To fully exploit the real-time computational capabilities of neural networks (NN) - as applied to image processing applications - a high performance VMEbus based analog neurocomputing architecture (VMENA) is developed.
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