Abstract
Multilayer perceptron neural networks possess pattern recognition properties that make them well suited for use in automatic target recognition systems. Their application is hindered, however, by the lack of a training algorithm, which reliably finds a nearly global optimal set of weights in a relatively short time. The approach presented here is based on implementation of genetic algorithms and fuzzy logic in training the proposed hybrid architecture. Compared to other approaches, it offers the following three main advantages. The neuro-computing technique is capable of fast and adaptive distortion-invariant pattern recognition for rapidly changing targets. On the other hand, genetic algorithms and fuzzy logic offer very sophisticated configuration control, which combines the results of previous computations with the external operating environment. Third, it allows us to significantly improve the reliability of object detection in the input scene with respect to associated distortions at no additional computational cost. This paper examines using genetic algorithms as an efficient way to train a feedforward neural net, the inputs for which are provided by a fuzzy front end, to be applied to automatic target recognition. The system is tested using actual laser detection and range data as training data and the results of the analysis show that the proposed system results in a much faster convergence on a weight set, and a high rate of successful recognition.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Abraham A (2001) Neuro-fuzzy systems: State-of-the-art modeling techniques connectionist models of neurons. In: Mira J, Prieto A (eds) Learning processes and artificial intelligence. Springer, Berlin, pp 269–276
Casasent D, Wang YC (2005) Automatic target recognition using new support vector machines. In: Proceedings IJCNN, pp 84–89
Davis LD (1988) Mapping classifier systems into neural networks. In: Proceedings of conference on neural information processing systems (NIPS’88). Morgan Kaufmann, San Mateo, pp 49–56
Feng Z, Shang-qian L, Da-bao W, Wei G (2009) Aircraft recognition in infrared image using wavelet moment invariants. Image Vis Comput 27:313–318
Guo Z, Xie W, Huang J (2008) Automatic target recognition of aircrafts using neural networks. In: Proceedings of IJCNN, pp 426–430
Hecht-Nielsen R (1990) Neurocomputing. Addison Wesley, Reading
Himes GS, Inigo RM (1992) Automatic target recognition using neocognitron. IEEE Trans Knowl Data Eng 4:167–172
Hoffmann F (2000) Soft computing techniques for the design of mobile robot behaviors. Inf Sci 122(2–4):241–258
Holland J (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge
Ishibuchi H, Nakashima T, Murata T (2001) Three-objective genetics-based machine learning for linguistic rule extraction. Inf Sci 136(1–4):109–133
Jain CT, Abraham A (2001) Adaptive database learning in decision support system using evolutionary fuzzy systems: a generic framework. In: First international workshop on hybrid intelligent system, pp 256–261
Jang J-S, Sun J-S, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice Hall, New York
Laine TI, Bauer KW (2008) A mathematical framework to optimize ATR systems with non-declarations and sensor fusion. Comput Oper Res 35:1789–1812
Marshall SJ, Harrison RF (1991) Optimization and training of feedforward neural networks by genetic algorithms. In: Second international conference on artificial neural networks, pp 39–43
Messer K, de Ridder D, Kittler J (1999) Adaptive texture representation methods for automatic target recognition. In: Proc British machine vision conference, pp 443–452
Mitchell M (2001) An introduction to genetic algorithms. A Bradford book, 7/e
Montana DJ, Davis L (1989) Training feedforward neural networks using genetic algorithms. In: Proceedings of the international joint conference on artificial intelligence, pp 762–767
Nicholas K, Treadgold T, Gedeon D (1998) Simulated annealing and weight decay in adaptive learning: the SARPROP algorithm. IEEE Trans Neural Netw 9(4):662–668
Ning W, Chen W (2003) Automatic target recognition of ISAR object images based on neural network. In: IEEE conference on neural networks and signal processing, pp 373–376
Olson C, Huttenlocher D (1997) Automatic target recognition by matching oriented edge pixels. IEEE Trans Image Process 6:103–113
Prasad S, Bruce LM (2008) Decision fusion with confidence-based weight assignment for hyperspectral target recognition. IEEE Trans Geosci Remote Sens 46(5):1448–1456
Radi A, Poli R (1998) Genetic programming can discover fast and general learning rules for neural networks. In: Third annual genetic programming conference (GP’98), pp 314–323
Sanderson C, Gibbins D, Searle S (2008) On statistical approaches to target silhouette classification in difficult conditions. Digit Signal Process 18:375–390
Sexton RS, Gupta JND (2000) Comparative evaluation of genetic algorithm and backpropagation for training neural networks. Inf Sci 129(1–4):45–59
Sexton R, Dorsey R, Johnson J (1999) Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing. Eur J Oper Res 114(3):589–601
Shapiro L, Stockman G (2001) Computer vision. Prentice Hall, New York
Snorrason M, Ruda H, Caglayan A (1995) Automatic target recognition in laser radar imagery. In: International conference on acoustics speech and signal processing, pp 2471–2474
Soliday SW, Perona MT, McCauley DG (1999) Hybrid fuzzy-neural classifier for feature level data fusion in LADAR. Autonomous target recognition, Raytheon Company Report
Srinivasa KG, Venugopal KR, Patnaik LM (2007) A self-adaptive migration model genetic algorithm for data mining applications. Inf Sci 177(20):4295–4313
Thierens D, Suykens J, Vandewalle J, DeMoor B (1993) Genetic weight optimization of a feedforward neural network controller. In: Proceedings of conference on artificial neural nets and genetic algorithms, pp 658–663
Whitley D (2001) An overview of evolutionary algorithms. J Inf Softw Technol 43:817–831
Whitley LD (1989) applying genetic algorithms to neural network learning. In: Proceedings of the 7th conference of the society of artificial intelligence and simulation of behavior, pp 137–144
Widrow B, Rumelhart DE, Lehr MA (1994) Neural networks: applications in industry business and science. Commun ACM 37(3):93–105
Yao X (1999) Evolving artificial neural networks. Proc IEEE 87:1423–1447
Yoon B, Holmes DJ, Langholz G, Kandel A (1994) Efficient genetic algorithms for training layered feedforward neural networks. Inf Sci 76:67–85
Zadeh L (1973) Outline of a new approach to analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern SMC-3-1973:28–44
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Valova, I., Milano, G., Bowen, K. et al. Bridging the fuzzy, neural and evolutionary paradigms for automatic target recognition. Appl Intell 35, 211–225 (2011). https://doi.org/10.1007/s10489-010-0213-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-010-0213-8