Effective Sonar Target Classification via Parallel Structure of Minimal Resource Allocation Network
In this paper, the processing of sonar signals has been
carried out using Minimal Resource Allocation Network (MRAN)
and a Probabilistic Neural Network (PNN) in differentiation of
commonly encountered features in indoor environments. The
stability-plasticity behaviors of both networks have been
investigated. The experimental result shows that MRAN possesses
lower network complexity but experiences higher plasticity than
PNN. An enhanced version called parallel MRAN (pMRAN) is
proposed to solve this problem and is proven to be stable in
prediction and also outperformed the original MRAN.
[1] R. P. Lippman, "An introduction to computing with neural nets", IEEE
ASSP Mag., pp. 4 - 22, Apr. 1987.
[2] W. W. L. Au, "Comparison of sonar discrimination - dolphin and
artificial neural network", J. Acoust. Soc. Amer., pt. 1, vol. 95, no. 5, pp.
2728 - 2735, 1994.
[3] H. L. Roitblat, W. W. L. Au, P. E. Nachtogall, R. Shizumura, and G.
Moons, "Sonar recognition of targets embedded in sediment", Neural
Netw., vol. 8, no. 7/8, pp. 1263 - 1273, 1995.
[4] J. A. Simmons, P. A. Saillant, J. M. Wotton, T. Haresign, M. J.
Feragamo, and C. F. Moss, "Composition of biosonar images for target
recognition by echolocating bats", Neural Netw., vol. 18, no. 7/8 , pp.
1239 - 1261, 1995.
[5] G. B. Willson, "Radar classification using a neural network", Proc.
SPIE, Optical Engineering and Photonics in Aerospace Sensing:
Application of Neural Networks, vol. 1294, pp. 200 - 210, 1990.
[6] S. Watanabe and M. Yoneyama, "An ultrasonic visual sensor for threedimensional
object recognition using neural networks", IEEE Trans.
Robot. Automat., vol. 8, pp. 240 - 249, Apr. 1992.
[7] I. E. Dror, M. Zagaeski, and C. F. Moss, "3-dimensional target
recognition via sonar - a neural network model", Neural Networks, vol.
8, no. 1, pp. 149 - 160, 1995.
[8] R. P. Gorman and T. J. Sejnowski, "Learned classification of sonar
targets using a massively parallel network", IEEE Trans. Acoust.,
Speech, Signal Processing, vol. 36, no. 7, pp. 1135 - 1140, 1998.
[9] B. Ayrulu and B. Barshan, "Identification of target primitives with
multiple decision-making sonars using evidential reasoning," Int. J.
Robot. Res., vol. 17, no. 6, pp. 598 - 623, 1998.
[10] L. Yingwei, N. Sundararajan, and P. Saratchandran, "Performance
evaluation of a sequential minimal Radial Basis Function (RBF) neural
network learning algorithm," IEEE Trans. Neural Networks, vol. 9, pp.
308-318, Mar. 1998.
[11] L. Yingwei, N. Sundararajan, and P. Saratchandran, "A sequential
learning scheme for function approximation using minimal radial basis
function neural networks," Neural Computing., vol. 9, pp. 461-478, Feb.
1997.
[12] M. Chan, C. Fung, "Incremental Adaption of Resource-Allocating
Network for Non-Stationary Time Series," Neural Networks, 1999.
IJCNN '99. IEEE International Joint Conference, Volume: 3 , 10-16,
July 1999, pp. 1554 -1559 vol.3
[13] B. L. Pulito, T. R. Damarla, S. Nariani, "A Two-Dimensional Shift
Invariant Image Classification Neural Network which overcomes the
Stability / Plasticity Dilemma," Neural Networks, 1990., 1990 IJCNN
International Joint Conference on 17-21 June 1990, pp. 825 -833, vol.2
[14] J. P. Albright, "An implementation and Evaluation of the ART1 Neural
Network For Pattern Recognition," Neural Networks, 1994. IEEE World
Congress on Computational Intelligence., 1994 IEEE International
Conference, Volume: 1 , 27 June-2 July 1994, pp. 498 -502, vol.1
[15] Youngtae Park, "An ART2 trained Two-Stage Learning on Circularly
Ordered Data Sequence," Neural Networks, 1994. IEEE World Congress
on Computational Intelligence., 1994 IEEE International Conference,
Volume: 5 , 27 June-2 July 1994, pp. 2928 -2933, vol.5
[16] Rok Rape, Dusan Fefer, Janko Drnovsek. Time Series Prediction with
Neural Networks: A Case Study of Two Examples, IMTC-94, IEEE,
May 1994.
[17] Ben Jacobsen. Time Series Properties of Stock Returns, Kluwer
BedrijfsInformatie, 1997.
[18] Donald F. Specht, "Enhancements To Probabilistic Neural Networks,"
Neural Networks, 1992. IJCNN., IEEE International Joint Conference,
Volume: 1 , 7-11, June 1992, pp. 761 - 768, vol.1
[19] B. Barshan, B. Ayrulu, and S. W. Utete, "Neural Network-Based Target
Differentiation Using Sonar for Robotics Applications," IEEE Trans.
Robotics and Automation, vol. 16, pp. 435 - 442, August 2000.
[20] N. Sundararajan, P. Saratchandran and, Lu Ying Wei, book of "Radial
Basis Function Neural Network," by School of Electrical & Electronic
Engineering, Nanyang Tech. Univ, S-pore.
[21] W. S. Lim, M.V.C. Rao, C.K. Loo,"Sequential Learning Neural
Network For Sonar Target Differentiation," Neural Network World 2/04,
187-197.
[22] D. F. Specht, "Probablistic Neural Networks," Neural Networks 3 (1990)
109-118.
[23] D. F. Specht, "Probabilistic neural networks for classification, mapping,
or associative memory," ICNN, vol.1 (San Diego, CA), July 1988, pp.
525-532.
[24] D. J. Marchette and C. E. Priebe, "The adaptive kernel neural network,"
Reports: AD-A217 230, 1989.
[25] P. S. Naloney, "The use of probabilistic neural networks to improve
solution times for hull-to-emitter correlation problems," IJCNN,
Sheraton Washington, Vol.1, 1989, pp. 289-294.
[26] D. F. Specht, "Application of probabilistic neural networks," SPIE,
Appl. Artif. Neural Networks 1294 (1990) 344-353.
[27] J. J. Guo, P. B. Luh, "Selecting input factors for clusters of Gaussian
radial basis function networks to improve market clearing price
prediction," Power Systems, IEEE Transactions, Volume 18, Issue 2,
May 2003 Page(s):665 - 672.
[28] G. B. Huang, P. Saratchandran, N. Sundararajan, "A generalized
growing and pruning RBF neural network for function approximation,"
Neural Networks, IEEE Transactions, Volume 16, Issue 1, Jan. 2005
Page(s):57 - 67.
[29] G. B. Huang, P. Saratchandran, N. Sundararajan, "An efficient
sequential learning algorithm for growing and pruning RBF networks,"
Systems, Man and Cybernetics, Part B, IEEE Transactions, Volume 34,
Issue 6, Dec. 2004 Page(s):2284 - 2292.
[1] R. P. Lippman, "An introduction to computing with neural nets", IEEE
ASSP Mag., pp. 4 - 22, Apr. 1987.
[2] W. W. L. Au, "Comparison of sonar discrimination - dolphin and
artificial neural network", J. Acoust. Soc. Amer., pt. 1, vol. 95, no. 5, pp.
2728 - 2735, 1994.
[3] H. L. Roitblat, W. W. L. Au, P. E. Nachtogall, R. Shizumura, and G.
Moons, "Sonar recognition of targets embedded in sediment", Neural
Netw., vol. 8, no. 7/8, pp. 1263 - 1273, 1995.
[4] J. A. Simmons, P. A. Saillant, J. M. Wotton, T. Haresign, M. J.
Feragamo, and C. F. Moss, "Composition of biosonar images for target
recognition by echolocating bats", Neural Netw., vol. 18, no. 7/8 , pp.
1239 - 1261, 1995.
[5] G. B. Willson, "Radar classification using a neural network", Proc.
SPIE, Optical Engineering and Photonics in Aerospace Sensing:
Application of Neural Networks, vol. 1294, pp. 200 - 210, 1990.
[6] S. Watanabe and M. Yoneyama, "An ultrasonic visual sensor for threedimensional
object recognition using neural networks", IEEE Trans.
Robot. Automat., vol. 8, pp. 240 - 249, Apr. 1992.
[7] I. E. Dror, M. Zagaeski, and C. F. Moss, "3-dimensional target
recognition via sonar - a neural network model", Neural Networks, vol.
8, no. 1, pp. 149 - 160, 1995.
[8] R. P. Gorman and T. J. Sejnowski, "Learned classification of sonar
targets using a massively parallel network", IEEE Trans. Acoust.,
Speech, Signal Processing, vol. 36, no. 7, pp. 1135 - 1140, 1998.
[9] B. Ayrulu and B. Barshan, "Identification of target primitives with
multiple decision-making sonars using evidential reasoning," Int. J.
Robot. Res., vol. 17, no. 6, pp. 598 - 623, 1998.
[10] L. Yingwei, N. Sundararajan, and P. Saratchandran, "Performance
evaluation of a sequential minimal Radial Basis Function (RBF) neural
network learning algorithm," IEEE Trans. Neural Networks, vol. 9, pp.
308-318, Mar. 1998.
[11] L. Yingwei, N. Sundararajan, and P. Saratchandran, "A sequential
learning scheme for function approximation using minimal radial basis
function neural networks," Neural Computing., vol. 9, pp. 461-478, Feb.
1997.
[12] M. Chan, C. Fung, "Incremental Adaption of Resource-Allocating
Network for Non-Stationary Time Series," Neural Networks, 1999.
IJCNN '99. IEEE International Joint Conference, Volume: 3 , 10-16,
July 1999, pp. 1554 -1559 vol.3
[13] B. L. Pulito, T. R. Damarla, S. Nariani, "A Two-Dimensional Shift
Invariant Image Classification Neural Network which overcomes the
Stability / Plasticity Dilemma," Neural Networks, 1990., 1990 IJCNN
International Joint Conference on 17-21 June 1990, pp. 825 -833, vol.2
[14] J. P. Albright, "An implementation and Evaluation of the ART1 Neural
Network For Pattern Recognition," Neural Networks, 1994. IEEE World
Congress on Computational Intelligence., 1994 IEEE International
Conference, Volume: 1 , 27 June-2 July 1994, pp. 498 -502, vol.1
[15] Youngtae Park, "An ART2 trained Two-Stage Learning on Circularly
Ordered Data Sequence," Neural Networks, 1994. IEEE World Congress
on Computational Intelligence., 1994 IEEE International Conference,
Volume: 5 , 27 June-2 July 1994, pp. 2928 -2933, vol.5
[16] Rok Rape, Dusan Fefer, Janko Drnovsek. Time Series Prediction with
Neural Networks: A Case Study of Two Examples, IMTC-94, IEEE,
May 1994.
[17] Ben Jacobsen. Time Series Properties of Stock Returns, Kluwer
BedrijfsInformatie, 1997.
[18] Donald F. Specht, "Enhancements To Probabilistic Neural Networks,"
Neural Networks, 1992. IJCNN., IEEE International Joint Conference,
Volume: 1 , 7-11, June 1992, pp. 761 - 768, vol.1
[19] B. Barshan, B. Ayrulu, and S. W. Utete, "Neural Network-Based Target
Differentiation Using Sonar for Robotics Applications," IEEE Trans.
Robotics and Automation, vol. 16, pp. 435 - 442, August 2000.
[20] N. Sundararajan, P. Saratchandran and, Lu Ying Wei, book of "Radial
Basis Function Neural Network," by School of Electrical & Electronic
Engineering, Nanyang Tech. Univ, S-pore.
[21] W. S. Lim, M.V.C. Rao, C.K. Loo,"Sequential Learning Neural
Network For Sonar Target Differentiation," Neural Network World 2/04,
187-197.
[22] D. F. Specht, "Probablistic Neural Networks," Neural Networks 3 (1990)
109-118.
[23] D. F. Specht, "Probabilistic neural networks for classification, mapping,
or associative memory," ICNN, vol.1 (San Diego, CA), July 1988, pp.
525-532.
[24] D. J. Marchette and C. E. Priebe, "The adaptive kernel neural network,"
Reports: AD-A217 230, 1989.
[25] P. S. Naloney, "The use of probabilistic neural networks to improve
solution times for hull-to-emitter correlation problems," IJCNN,
Sheraton Washington, Vol.1, 1989, pp. 289-294.
[26] D. F. Specht, "Application of probabilistic neural networks," SPIE,
Appl. Artif. Neural Networks 1294 (1990) 344-353.
[27] J. J. Guo, P. B. Luh, "Selecting input factors for clusters of Gaussian
radial basis function networks to improve market clearing price
prediction," Power Systems, IEEE Transactions, Volume 18, Issue 2,
May 2003 Page(s):665 - 672.
[28] G. B. Huang, P. Saratchandran, N. Sundararajan, "A generalized
growing and pruning RBF neural network for function approximation,"
Neural Networks, IEEE Transactions, Volume 16, Issue 1, Jan. 2005
Page(s):57 - 67.
[29] G. B. Huang, P. Saratchandran, N. Sundararajan, "An efficient
sequential learning algorithm for growing and pruning RBF networks,"
Systems, Man and Cybernetics, Part B, IEEE Transactions, Volume 34,
Issue 6, Dec. 2004 Page(s):2284 - 2292.
@article{"International Journal of Information, Control and Computer Sciences:52393", author = "W.S. Lim and M.V.C. Rao", title = "Effective Sonar Target Classification via Parallel Structure of Minimal Resource Allocation Network", abstract = "In this paper, the processing of sonar signals has been
carried out using Minimal Resource Allocation Network (MRAN)
and a Probabilistic Neural Network (PNN) in differentiation of
commonly encountered features in indoor environments. The
stability-plasticity behaviors of both networks have been
investigated. The experimental result shows that MRAN possesses
lower network complexity but experiences higher plasticity than
PNN. An enhanced version called parallel MRAN (pMRAN) is
proposed to solve this problem and is proven to be stable in
prediction and also outperformed the original MRAN.", keywords = "Ultrasonic sensing, target classification, minimalresource allocation network (MRAN), probabilistic neural network(PNN), stability-plasticity dilemma.", volume = "2", number = "6", pages = "1879-8", }
