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Use of A algorithm for obstacle avoidance


Posted by: nit_cal
Created at: Friday 30th of October 2009 05:56:55 AM
Last Edited Or Replied at :Friday 30th of October 2009 05:56:55 AM
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istance.We look at our cost function and find the minimum cost D for moving from one space to an adjacent space. Therefore, the heuristic in this case should be D times the Manhattan distance:
h(n) = D * (abs(n.x-goal.x) + abs(n.y-goal.y))
• If on our map we allow diagonal movement we need a different heuristic. The Manhattan distance for (4 cast, A north) will be 8*D. However, we could simply move (4 northeast)
instead, so the heuristic should be 4*D. This function handles diagonals, assuming that
both straight and diagonal movement costs.D:
h{n) = D * max(abs(n.x-goal.x), abs(n.y-goal..................[:=> Show Contents <=:]



GENETIC PROGRAMMING A SEMINAR REPORT


Posted by: Computer Science Clay
Created at: Saturday 13th of June 2009 03:13:46 PM
Last Edited Or Replied at :Tuesday 28th of February 2012 10:05:49 PM
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tances is to zero, the better the S-expression. If the S-expression (or each component of a vector or list) is real-valued or integer-valued, the square root of the sum of squares of distances can, alternatively, be used to measure fitness (thereby increasing the influence of more distant points). For some problems described herein, the fitness function does not work with the actual value returned by the individual S-expression, but some number (e.g. elapsed time, total score, cases handled, etc.) which is indirectly created by the action of the S-expression. Nonetheless, the raw fitness fun..................[:=> Show Contents <=:]



Securing the Network Routing Algorithms Download Full Seminar Report


Posted by: computer science crazy
Created at: Thursday 09th of April 2009 02:28:25 AM
Last Edited Or Replied at :Thursday 09th of April 2009 02:28:25 AM
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ge]Downlaod

Mirror ..................[:=> Show Contents <=:]



Self Organizing Maps


Posted by: computer science crazy
Created at: Wednesday 08th of April 2009 12:13:21 AM
Last Edited Or Replied at :Wednesday 08th of April 2009 12:13:21 AM
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ctory background material on statistical pattern recognition. The terms and concepts will be useful in understanding the later material on unsupervised neural networks. As the approach underlying unsupervised networks is the measurement of how similar (or different) various inputs are, we need to consider how the distances between these inputs are measured. This forms the basis Section Three, together with a brief description of non-neural approaches to unsupervised learning. Section Four discusses the background to and basic algorithm of Kohonen self-organizing maps. The next section details ..................[:=> Show Contents <=:]



WEB MINING


Posted by: seminar projects crazy
Created at: Friday 30th of January 2009 01:22:16 PM
Last Edited Or Replied at :Saturday 16th of February 2013 12:13:10 AM
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some years these possibilities were used mostly in the scientific world but recent years have seen an immense growth in popularity, supported by the wide availability of computers and broadband communication. The use of the internet for other tasks than finding information and direct communication is increasing, as can be seen from the interest in ?e-activities? such as e-commerce, e-learning, e-government, e-science. Independently of the development of the Internet, Data Mining expanded out of the academic world into industry. Methods and their potential became known outside the academic worl..................[:=> Show Contents <=:]



HEURISTIC ALGORITHM FOR CLIQUE PROBLEM


Posted by: seminar projects crazy
Created at: Friday 30th of January 2009 12:13:43 PM
Last Edited Or Replied at :Friday 30th of January 2009 12:13:43 PM
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er P ? NP question has been one of the deepest, most perplexing open research problems in theoretical Computer Science since it was posed in 1971...................[:=> Show Contents <=:]



Fast Convergence Algorithms for Active Noise Controlin Vehicles


Posted by: computer science crazy
Created at: Sunday 21st of September 2008 11:57:31 PM
Last Edited Or Replied at :Sunday 21st of September 2008 11:57:31 PM
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ptation step size, in order to obtain the same convergence speed for all of them...................[:=> Show Contents <=:]



Neural Networks And Their Applications


Posted by: computer science crazy
Created at: Sunday 21st of September 2008 11:28:39 PM
Last Edited Or Replied at :Sunday 21st of September 2008 11:28:39 PM
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d the logician Walter Pitts.

Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an expert in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer what if questions.
Other advantages include: -

1. Adaptive learning: An ability to learn how to do tasks based on..................[:=> Show Contents <=:]



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