Abstract
A community intrusion detection system based on radial basic probabilistic neural network (RBPNN) is presented in this paper. This system is composed of ARM (Advanced RISC Machines) data acquisition nodes, wireless mesh network and control centre. The sensor is used to collect information in the data acquisition node and processes them by image detection algorithm, and then transmits information to control centre with wireless mesh network. When there is abnormal phenomenon, the system starts the camera and the radial basic probabilistic neural network algorithm is used to recognize the face image. We construct the structure of RBPNN that used for recognition face image, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. With the ability of strong pattern classification and function approach and fast convergence of RBPNN, the recognition method can truly classify the face. This system resolves the defect and improves the intelligence and alleviates worker’s working stress.
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© 2009 Springer-Verlag Berlin Heidelberg
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Gao, M., Tian, J., Zhou, S. (2009). Community Intrusion Detection System Based on Radial Basic Probabilistic Neural Network. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_84
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DOI: https://doi.org/10.1007/978-3-642-01510-6_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
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