Applications of Neural Networks by Alan Murray (auth.), Alan F. Murray (eds.)
By Alan Murray (auth.), Alan F. Murray (eds.)
Applications of Neural Networks offers a close description of thirteen useful purposes of neural networks, chosen as the initiatives played through the neural networks are actual and important. The contributions are from major researchers in neural networks and, as a complete, offer a balanced assurance throughout a number software components and algorithms. The booklet is split into 3 sections. part A is an creation to neural networks for nonspecialists. part B seems to be at examples of functions utilizing `Supervised Training'. part C offers a few examples of `Unsupervised Training'.
For neural community fans and , open-minded sceptics. The booklet leads the latter during the basics right into a convincing and sundry sequence of neural luck tales -- defined conscientiously and in truth with out over-claiming. Applications of Neural Networks is key analyzing for all researchers and architects who're tasked with utilizing neural networks in actual existence purposes.
Read or Download Applications of Neural Networks PDF
Similar networks books
Entire suggestions for laptop Networks (4th variation) by way of Andrew Tanenbaum.
This ebook and its sister quantity acquire refereed papers awarded on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. construction at the good fortune of the former six successive ISNN symposiums, ISNN has turn into a well-established sequence of well known and high quality meetings on neural computation and its purposes.
Advances in networking effect many types of tracking and keep an eye on platforms within the such a lot dramatic approach. Sensor community and configuration falls less than the class of recent networking platforms. instant Sensor community (WSN) has emerged and caters to the necessity for real-world functions. technique and layout of WSN represents a extensive study subject with functions in lots of sectors reminiscent of undefined, domestic, computing, agriculture, surroundings, and so forth, in line with the adoption of primary rules and the cutting-edge know-how.
- Artificial neural networks - industrial and control engineering applications
- [(New Trends in Computer Networks )] [Author: T. Tugcu] [Oct-2005]
- Inter-Asterisk Exchange (IAX): Deployment Scenarios in SIP-Enabled Networks
- Frame Relay Networks [Signature ed.]
- Know Thy Enemy II A Look at the World's Most Threatening Terrorist Networks and Criminal Gangs
Extra info for Applications of Neural Networks
Kohonen, Self-organisation and Associative Memory, SpringerVerlag, Berlin, 1984. 19. E. E. J. Williams, "Learning Internal Representations by Error Propagation", Parallel Distributed Processing :Explorations in the Microstructure of Cognition, vol. 1, pp. 318- 362, 1986. 31 20. J. W. Anderson, "An alternative to backpropagation : A simple rule for synaptic modification for neural net training and memory", Internal Report, Univ. , 1989. 21. J. Werbos, "A menu for designs of reinforcement learning over time", in Neural networks for control, ed.
F. Murray, A. J. Baxter, S. M. Reekie, and L. Tarassenko, "Integrated Pulse-Stream Neural Networks - Results, Issues and Pointers", IEEE Trans. Neural Networks, pp. 385-393, 1992. 36. J. Meador, A. Wu, C. Cole, N. Nintunze, and P. Chintrakulchai, "Programmable Impulse Neural Circuits", IEEE Transactions on Neural Networks, vol. 2, no. 1, pp. 101-109, 1990. 37. M. Reyneri, F. Gregoreti, and C. Truzzi, "Interfacing Sensors and Actuators to CPWM and CPEM Neural Networks", Proc. International Conference on Microelectronics for Neural Networks, Edinburgh, pp.
1 0. TEACHER (a) ~: . ~: ~. ~ .. GOOD/BAD? (b) r············· ~: ~: ~: ~ .. 10 : (a) Supervised training by a teacher and (b) Reinforcement training with the help of a critic (schematic). In conventional, fully-supervised learning- such as is shown in Fig. 9(a) - 23 the teacher (who generated the training set) calculates an error signal and passes it back into the NN for back-propagation. In reinforcement learning (Fig. 9b), the "teacher" simply offers up a criticism of the result of a network's action, which is then used to determine the next set of weight changes.