Idea Generation Algorithm Based Systems

Usha A. Jogalekar; Akhil Mangla

doi:10.4028/www.scientific.net/AMR.403-408.3937

Paper Titles

A Communication Network Design for Robotics through a Queen Bees Algorithm
p.3909

Design and Fabrication of an Autonomous Mobile Robot for Obstacle Avoidance and Path Planning in Partially Known Environment
p.3917

Generalization Backstepping Method Based on Stabilization of Parameters Perturbation Lu Chaos Using Adaptive Neuro-Fuzzy Inference System
p.3925

Brain Controlled Car for Disabled Using Artificial Intelligence
p.3932

Idea Generation Algorithm Based Systems
p.3937

FAutoREDWithRED: An Algorithm to Increase the Fairness in Internet Routers
p.3946

An Algorithm to Determine Placement Sequence in Robotic Pallet Pattern Formation
p.3953

Daugman’s Algorithm Enhancement for Iris Localization
p.3959

On Line Application of Profit Based Unit Commitment Using Hybrid Algorithms of Memory Management Algorithm
p.3965

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Idea Generation Algorithm Based Systems

Idea Generation Algorithm Based Systems

Abstract:

Parallel algorithms and parallel processing have revolutionized the machine’s performance and output efficiency. Parallel Random Access Machines are being used excessively for complex input processing and effective output data generation. PRAM algorithms are a class of algorithms defined for parallel computation in polynomial time complexity. Thought process is one of the key procedure that distinctly identifies humans rather animals from machines. Machines proposed to be effective computers have failed when it has come in light to learn and produce new ideas. The study here proposes the Idea Generation Algorithm with all necessary details. The algorithm uses parallel set of processors with a systolic array for data processing. The recognition of optimal output based on the associated weights and the feedback provided by to self-organizing neural network. The network provided with an unsupervised learning is proposed to provide the machine with its own set of ideas thus resulting in achievement of “thought process” in Machines. The algorithm stands as a template for any thought process development and identification in machines. Any system with capability and use of idea generation algorithm shall be with inherent learning and intelligence.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

3937-3945

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.3937

Citation:

Cite this paper

Online since:

November 2011

Authors:

Usha A. Jogalekar, Akhil Mangla

Keywords:

Idea Generation Algorithm, Lateral Thinking, Neural Network (NN), Parallel Processors, Thought Process

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Richard F. Gilberg, Behrouz A. Forouzan Data Structures: A Pseudocode Approach with C, Second Edition 2005 by Course Technology a part of Cengage Learning, p.239.

Google Scholar

[2] Heng-Li Yang, Cheng-Hwa Lee, An Idea Generation Supporting System based on Anchor and Gestalt Theories, MIS Dept., National Cheng-Chi University.

Google Scholar

[3] Ho Cheong Lee, Ming Xi Tang, GENERATING STYLISTICALLY CONSISTENT PRODUCT FORM DESIGNS USING INTERACTIVE EVOLUTIONARY PARAMETRIC SHAPE GRAMMARS, " CAIDCD , 06. 7th International Conference onComputer-Aided Industrial Design and Conceptual Design, (2006).

DOI: 10.1109/caidcd.2006.329391

Google Scholar

[4] E. De Bono, Lateral Thinking, Penguin Publishing House, First Edition, pp.1-32.

Google Scholar

[5] Russell Norvig, Artificial Intelligence, Second Edition, Pearson Prentice Hall.

Google Scholar

[6] Ramkrishna R. Gekhre J., Database Management Systems, Third Edition, Tata Mac Graw Hill, (2003).

Google Scholar