FPGA Implementation of A Pulse Density Neural Network With Learning Ability Using Simultaneous Perturbation  

Oleh:

Maeda, Y. ; Tada, T.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 3 (May 2003), page 688-695.
Topik: neural network; CURRENT DENSITY; FPGA implementation; pulse density; neural network; learning ability; perturbation

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Hardware realization is very important when considering wider applications of neural networks (NN s). In particular, hardware NN s with a learning ability are intriguing. In these networks, the learning scheme is of much interest, with the backpropagation method being widely used. A gradient type of learning rule is not easy to realize in an electronic system, since calculation of the gradients for all weights in the network is very difficult. More suitable is the simultaneous perturbation method, since the learning rule requires only forward operations of the network to modify weights unlike the backpropagation method. In addition, pulse density NN systems have some promising properties, as they are robust to noisy situations and can handle analog quantities based on the digital circuits. We describe a field - programmable gate array realization of a pulse density NN using the simultaneous perturbation method as the learning scheme. We confirm the viability of the design and the operation of the actual NN system through some examples.

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