Multi-Pattern Real-Valued Spectral Associative Memories
نویسنده
چکیده
A multi-pattern encoding and decoding scheme is presented that extends the family of spectral associative memories (SAMs) to include gray-level, or analog patterns. SAMs are frequency-domain formulations of associative memory that combine the extrinsic redundancy of neural networks with the in-phase, quadrature, and complex modulation schemes of communications. Considerable coding gain occurs at the level of modulation and these networks may be regarded as multi-channel, multi-carrier generalizations of amplitude modulation. Unlike multi-pattern bipolar SAMs, which are exclusively content-addressable, real-valued SAMs also have an address-addressable mode in which the recall of a particular memory may be forced. Band structures and anti-aliasing constraints are presented along with a probabilistic formulation in which virtual entanglement is a natural feature. Simulations are presented that demonstrate dual-memory recall for 6x6 gray-level patterns.
منابع مشابه
Associative Amplitude Modulation with Built-In Noise Immunity
Single-pattern real-valued spectral associative memories (SAMs) are proposed for coding and recalling sampled analog or multi-valued data patterns over noisy channels. SAMs are frequencydomain formulations of associative memory that combine the extrinsic redundancy of neural networks with the in-phase and quadrature modulation schemes of telecommunications. Data patterns, or “codewords”, are en...
متن کاملDesign of Multi-valued Cellular Neural Networks for Associative Memories
Cellular neural networks (CNNs) are one type of interconnected neural network and differ from the well-known Hopfield model in that each cell has a piecewise linear output function. In this paper, we present a multi-valued CNN model in which each nonlinear element consists of a multi-valued output function. The function is defined by a linear combination of piecewise linear functions. We conduc...
متن کاملBipolar spectral associative memories
Nonlinear spectral associative memories are proposed as quantized frequency domain formulations of nonlinear, recurrent associative memories in which volatile network attractors are instantiated by attractor waves. In contrast to conventional associative memories, attractors encoded in the frequency domain by convolution may be viewed as volatile online inputs, rather than nonvolatile, off-line...
متن کاملRobust image retrieval from noisy inputs using lattice associative memories
Lattice associative memories also known as morphological associative memories are fully connected feedforward neural networks with no hidden layers, whose computation at each node is carried out with lattice algebra operations. These networks are a relatively recent development in the field of associative memories that has proven to be an alternative way to work with sets of pattern pairs for w...
متن کاملOn the EXISTENCE OF HOPFIELD NEURAL NETWORKS: SYNTHESIS OF HOPFIELD TYPE ASSOCIATIVE MEMORIES:
In this research paper, the problem of existence of the associative memory synthesized by Hopfield is addressed and solved. Using Hadamard matrix of suitable dimension, an algorithm to synthesize real valued Hopfield neural network is discussed. The problem of existence and synthesis of a certain complex Hopfield neural network is addressed and solved. Also, synthesis of real and complex Hopfie...
متن کامل