By Kenji Suzuki (editor)
Read Online or Download Artificial neural networks: Architectures and applications PDF
Similar signal processing books
Radio Frequency (RF) is the elemental know-how at the back of an important variety of contemporary patron electronics and instant conversation units, and this booklet offers a accomplished and methodical advisor to RF for engineers, technicians, fanatics and hobbyists with an curiosity within the electronics at the back of radio frequency communications.
Advances in Imaging and Electron Physics merges long-running serials--Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. This sequence gains prolonged articles at the physics of electron units (especially semiconductor devices), particle optics at low and high energies, microlithography, photograph technological know-how and electronic photograph processing, electromagnetic wave propagation, electron microscopy, and the computing equipment utilized in most of these domain names.
A major operating source for engineers and researchers thinking about the layout, improvement, and implementation of sign processing platforms the decade has noticeable a swift enlargement of using box programmable gate arrays (FPGAs) for a variety of purposes past conventional electronic sign processing (DSP) structures.
- Applications of Random Process Excursion Analysis
- Signal Processing: A Mathematical Approach
- Digital Signal Processing Using the ARM Cortex M4
- Optics of Charged Particle Analyzers
- Digital signal compression : principles and practice
- Bootstrap Techniques for Signal Processing
Additional info for Artificial neural networks: Architectures and applications
1997). A proposal of novel knowledge representation (Area representation) and the implementation by neural network. International Con‐ ference on Computational Intelligence and Neuroscience, III, 430-433. , & Osana, Y. (2008). Implementation of association of one-to-many as‐ sociations and the analog pattern in Kohonen feature map associative memory with area representation. Proceedings of IASTED Artificial Intelligence and Applications, Inns‐ bruck. , & Osana, Y. (2010). Kohonen feature map probabilistic associative memo‐ ry based on weights distribution.
In supervised learning, the input is associated with the output. If they are equal, learning is called auto-associative; if they are different, hetero-associative. 6. Back-propagation Back-propagation (BP) is a supervised algorithm for multilayer networks. It applies the generalized delta rule, requiring two passes of computation: (1) activation propagation (forward pass), and (2) error back propagation (backward pass). Back-propagation works in the following way: it propagates the activation from input to hidden layer, and from hidden to output layer; calculates the error for output units, then back propagates the error to hidden units and then to input units.
2. The perceptron Rosenblatt’s perceptron  takes a weighted sum of neuron inputs, and sends output 1 (spike) if this sum is greater than the activation threshold. It is a linear discriminator: given 2 points, a straight line is able to discriminate them. For some configurations of m points, a straight line is able to separate them in two classes (figures 3 and 4). Figure 3. Set of linearly separable points. Figure 4. Set of non-linearly separable points. The limitations of the perceptron is that it is an one-layer feed-forward network (non-recurrent); it is only capable of learning solution of linearly separable problems; and its learning algorithm (delta rule) does not work with networks of more than one layer.
Artificial neural networks: Architectures and applications by Kenji Suzuki (editor)