A study on the modeling ability of the IDS method: A soft computing technique using pattern-based information processing

The ink drop spread (IDS) method is a modeling technique developed by algorithmically mimicking the information-handling processes of the human brain. This method has been proposed as a new approach to soft computing. IDS modeling is characterized by processing that uses intuitive pattern information instead of complex formulas, and it is capable of stable and fast convergences. This paper investigates the modeling ability of the IDS method based on three typical benchmarks. Experimental results demonstrated that the IDS method can handle various modeling targets, ranging from logic operations to complex nonlinear systems, and that its modeling performance is satisfactory in comparison with that of feedforward neural networks.

[1]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[2]  Saeed Bagheri Shouraki,et al.  A novel fuzzy approach to modeling and control and its hardware implementation based on brain functionality and specifications , 2000 .

[3]  Sameer Singh.  Classification of helical structures , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  José R. Álvarez.  Injecting Knowledge into the Solution of the Two-Spiral Problem , 1999, Neural Computing & Applications.

[6]  Jenq-Neng Hwang,et al.  Regression modeling in back-propagation and projection pursuit learning , 1994, IEEE Trans. Neural Networks.

[7]  Khashayar Khorasani,et al.  Constructive feedforward neural networks using Hermite polynomial activation functions , 2005, IEEE Transactions on Neural Networks.

[8]  Korris Fu-Lai Chung,et al.  Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning , 2001, IEEE Trans. Fuzzy Syst..

[9]  Lotfi A. Zadeh,et al.  Soft computing and fuzzy logic , 1994, IEEE Software.

[10]  Jenq-Neng Hwang,et al.  The cascade-correlation learning: a projection pursuit learning perspective , 1996, IEEE Trans. Neural Networks.

[11]  Nakaji Honda,et al.  Fuzzy Interpretation of Human Intelligence , 1999, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[12]  Joachim Diederich,et al.  The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks , 1998, IEEE Trans. Neural Networks.

[13]  K. Lang,et al.  Learning to tell two spirals apart , 1988 .

[14]  Masayuki Murakami,et al.  Classification performance of the IDS method based on the two-spiral benchmark , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[15]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[16]  Nakaji Honda,et al.  Recursive Fuzzy Modeling Based on Fuzzy Interpolation , 1999, J. Adv. Comput. Intell. Intell. Informatics.

[17]  Masayuki Murakami,et al.  A Study on the Real-Time Modeling Capabilities of the IDS Method , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[18]  James T. Kwok,et al.  Objective functions for training new hidden units in constructive neural networks , 1997, IEEE Trans. Neural Networks.

[19]  Jiancheng Jia,et al.  Solving two-spiral problem through input data representation , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.