0000000000059212
AUTHOR
Ke Qin
Networking logistic neurons can yield chaotic and pattern recognition properties
Accepted version of an article the book: 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings. Published version available from IEEE: http://dx.doi.org/10.1109/CIMSA.2011.6059914 Over the last few years, the field of Chaotic Neural Networks (CNNs) has been extensively studied because of their potential applications in the understanding/recognition of patterns and images, their associative memory properties, their relationship to complex dynamic system control, and their capabilities in the modeling and analysis of other measurement systems. However, the results concerning CNNs which can demonstrate chaos, quasi-chaos, …
On the Cryptanalysis of Two Cryptographic Algorithms That Utilize Chaotic Neural Networks
This paper deals with the security and efficiency issues of two cipher algorithms which utilize the principles of Chaotic Neural Networks (CNNs). The two algorithms that we consider are (1) the CNN-Hash, which is a one-way hash function based on the Piece-Wise Linear Chaotic Map (PWLCM) and the One-Way Coupled Map Lattice (OCML), and (2) the Delayed CNN-Based Encryption (DCBE), which is an encryption algorithm based on the delayed CNN. Although both of these cipher algorithms have their own salient characteristics, our analysis shows that, unfortunately, the CNN-Hash is not secure because it is neither Second-Preimage resistant nor collision resistant. Indeed, one can find a collision with …
Ideal Chaotic Pattern Recognition is achievable: The Ideal-M-AdNN - its design and properties
Published version of a chapter in the book: Transactions on Computational Collective Intelligence XI. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-41776-4_2 This paper deals with the relatively new field of designing a Chaotic Pattern Recognition (PR) system. The benchmark of such a system is the following: First of all, one must be able to train the system with a set of “training” patterns. Subsequently, as long as there is no testing pattern, the system must be chaotic. However, if the system is, thereafter, presented with an unknown testing pattern, the behavior must ideally be as follows. If the testing pattern is not one of the trained patterns, the system …