Novel input and output mapping-sensitive error back propagation learning algorithm for detecting small input feature variations

This paper proposes a new error back propagation learning algorithm that properly enhances the sensitivity of input and output mapping by applying a high-pass filter characteristic to the conventional error back propagation learning algorithm, allowing small input feature variations to be successfully indicated. For the sensitive discrimination of novel class data with slightly different characteristics from the normal class data, the cost function in the proposed neural network algorithm is modified by further increasing the input and output sensitivity, where weight update rules are used to minimize the cost function using a gradient descent method. The proposed algorithm is applied to an auto-associative multilayer perceptron neural network and its performance evaluated with two real-world applications: a laser spot detection-based computer interface system for detecting a laser spot in complex backgrounds and an automatic inspection system for the reliable detection of Mura defects that occur during the manufacture of flat panel liquid crystal displays. When compared with the conventional error back propagation learning algorithm, the proposed algorithm shows a better performance as regards detecting small input feature variations by increasing the input–output mapping sensitivity.

[1]  Jong Gwan Lim,et al.  Fast and Reliable Camera-tracked Laser Pointer System Designed for Audience , 2008 .

[2]  Minho Lee,et al.  Input and Output Mapping Sensitive Auto-Associative Multilayer Perceptron for Computer Interface System Based on Image Processing of Laser Pointer Spot , 2010, ICONIP.

[3]  Charles Weber,et al.  An integrated framework for yield management and defect/fault reduction , 1995 .

[4]  Mao-Jiun J. Wang,et al.  The development of an automatic post-sawing inspection system using computer vision techniques , 1999 .

[5]  M. A. Javed,et al.  Process monitoring using auto-associative, feed-forward artificial neural networks , 1993, J. Intell. Manuf..

[6]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[7]  Harris Drucker,et al.  Improving generalization performance using double backpropagation , 1992, IEEE Trans. Neural Networks.

[8]  T. Marwala,et al.  Fault classification in structures with incomplete measured data using autoassociative neural networks and genetic algorithm , 2006 .

[9]  Chao-Ton Su,et al.  A Neural-Network Approach for Defect Recognition in TFT-LCD Photolithography Process , 2009, IEEE Transactions on Electronics Packaging Manufacturing.

[10]  Minho Lee,et al.  Laser spot pattern recognition based computer interface using I/O mapping sensitive neural networks , 2011, 2011 IEEE International Conference on Consumer Electronics (ICCE).

[11]  Vadlamani Ravi,et al.  Modified Great Deluge Algorithm based Auto Associative Neural Network for Bankruptcy Prediction in Banks , 2007 .

[12]  Dan R. Olsen,et al.  Laser pointer interaction , 2001, CHI.

[13]  Taho Yang,et al.  A neural-network approach for semiconductor wafer post-sawing inspection , 2002 .

[14]  Jean-François Lapointe,et al.  On-screen laser spot detection for large display interaction , 2005, IEEE International Workshop on Haptic Audio Visual Environments and their Applications.

[15]  Heinrich Müller,et al.  Interaction with a projection screen using a camera-tracked laser pointer , 1998, Proceedings 1998 MultiMedia Modeling. MMM'98 (Cat. No.98EX200).

[16]  Byung-Gook Lee,et al.  Laser Pointer Interaction System Based on Image Processing , 2008 .

[17]  Peter Tiño,et al.  Measuring Generalization Performance in Coevolutionary Learning , 2008, IEEE Transactions on Evolutionary Computation.

[18]  Frederick Y. Wu,et al.  Automatic defect classification for semiconductor manufacturing , 1997, Machine Vision and Applications.

[19]  Fei-Long Chen,et al.  A neural-network approach to recognize defect spatial pattern in semiconductor fabrication , 2000 .

[20]  Alex Zelinsky,et al.  Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf] , 2009, IEEE Robotics & Automation Magazine.