Exponential Bidirectional Associative Memory Research Articles

Generalized Exponential Bidirectional Fuzzy Associative Memory with Fuzzy Cardinality-Based Similarity Measures Applied to Face Recognition

Associative memories are biologically inspired models designed for the storage and recall by association. Such models aim to store a finite set of associations, called the fundamental memory set. The generalized exponential bidirectional fuzzy associative memory (GEB-FAM) is a heteroassociative memory model designed for the storage and recall of fuzzy sets. A similarity measure, that is, a function that indicates how much two fuzzy sets are equal, is at the core of a GEB-FAM model. In this paper, we present a detailed study on the use of cardinality-based similarity measures in the definition of a GEB-FAM. Moreover, we evaluate the performance of the GEB-FAMs defined using such measures in a face recognition problem.

A Fuzzy Knowledge Based Fault Tolerance Mechanism for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are the focus of considerable research for different applications. This paper proposes a Fuzzy Knowledge based Artificial Neural Network Routing (ANNR) fault tolerance mechanism for WSNs. The proposed method uses an exponential Bi-directional Associative Memory (eBAM) for the encoding and decoding of data packets and application of Intelligent Sleeping Mechanism (ISM) to conserve energy. A combination of fuzzy rules is used to identify the faulty nodes in the network. The Cluster Head (CH) acts as the data aggregator in the network. It applies the fuzzy knowledge based Node Appraisal Technique (NAT) in order to identify the faulty nodes in the network. The performance of the proposed ANNR is compared with that of Low-Energy Adaptive Clustering Hierarchy (LEACH), Dual Homed Routing (DHR) and Informer Homed Routing (IHR) through simulation.

Guaranteed recall of all training pairs for exponential bidirectional associative memory

Several methods of guaranteeing the recall of all training pairs for exponential bidirectional associative memory are presented. If the minimum Hamming distance between patterns is known, the radix can be determined to provide a certain basin of attraction. Otherwise the statistical minimum distance can be applied. A weight learning algorithm based on a pseudo-relaxation algorithm is also developed to make all training pairs recallable with a small radix.

Capacity of various exponential bidirectional associative memories

Bidirectional associative memory (BAM) has been widely used to solve classification problems such as pattern recognition, data compression, etc. Since Jeng et al. (1990) proposed the exponential BAM (eBAM), which has a high storage capacity and error-correction capability as well as being simple to implement, a great deal of research has gone into further improving its performance. The capacities of various eBAMs are derived and compared.

Design of an inner-product processor for hardware realization of multi-valued exponential bidirectional associative memory

Inner-product calculations are often required in digital neural computing. The critical path of the inner product of two vectors is the carry propagation delay generated from individual product terms. In this work, a novel and high-speed realization of an inner-product processor for the multi-valued exponential bidirectional associative memory (MV-eBAM) is presented in order to reduce the carry propagation delay, wherein the treatment of inner product of two vectors is given. Notably, a systolic-like architecture of digital compressors is used to reduce the carry propagation delay in the critical path of the inner product of two vectors. The architecture we propose here might offer a sub-optimal solution for the digital hardware realization of the inner-product computation.

A deterministic capacity-finding method for multi-valued exponential BAM

The multi-valued exponential bidirectional associative memory (MV-eBAM) has been proposed and proved to be asymptotically stable under certain constraints. Although multi-valued eBAM has been verified to possess high capacity by thorough simulations, the capacity is still unable to be solved analytically. In the paper, an algorithm is proposed to derive the capacity. Some important characteristics, including the absolute lower bound of the radix, and the approximate capacity are also discussed. The result shows that the multi-valued eBAM indeed possesses high capacity.

Data compression by the recursive algorithm of exponential bidirectional associative memory

A novel data compression algorithm utilizing the histogram and the high-capacity exponential bidirectional associative memory (eBAM) is presented. Since eBAM has been proved to possess high capacity and fault tolerance, it is suitable to be utilized in the data compression using the table-lookup scheme. The histogram approach is employed to extract the feature vectors in the given data. The result of the simulation of the proposed algorithm turns out to be better than the traditional methods.

Analysis of radix searching of exponential bidirectional associative memory

The exponential bidirectional associative memory (eBAM) is a high-capacity associative memory. However, in the hardware realisation of eBAM, increasing efforts have been made to obtain an optimally small radix of exponential circuit for the fixed dynamic range of the VLSI circuit transistor, thereby allowing the dimension of the stored patterns to reach maximum. In this paper, the authors prove the stability of eBAM. The absolute lower bound of the radix of the eBAM is also obtained. In addition, an algorithm is presented to compute the optimal radix of an exponential circuit. To preserve the optimality of the radix, an algorithm capable of updating the radix when new pattern pairs are to be installed is proposed. Moreover, a deterministic method is presented to train and install pattern pairs with a predetermined fault tolerance ability.

Improved exponential bidirectional associative memory

Based on Jeng's exponential bidirectional associative memory (eBAM) another improved updating rule for eBAMs is presented. In the recalling process of the improved eBAM (IeBAM), the continuity assumption of the eBAM is avoided, and the stability of the system in synchronous and asynchronous modes are proven by defining an energy function which decreases on the change of neuron states. The proposed model greatly improves the performances of the eBAM. Computer simulations demonstrate that the IeBAM has a much higher storage capacity and a better error correcting capability than those of the eBAM.

Digital design of discrete exponential bidirectional associative memory

The exponential bidirectional associative memory (eBAM) was proved to be a systematically stable high-capacity memory. Considering the implementation of such an eBAM, we adopt the digital logic methodology which is based on several factors, such as scalability and speed. In order to realize the eBAM by digital circuitry only, some special design is required such that the exponential function can be implemented without the loss of operating speed. For example, the exponent 8-to-9 high-speed value generator. Besides, the traditional add/sub accumulator costs too much area when the dimension of patterns is large. A pipelined increment/decrement accumulator is proposed in the design, which can also speed up the addition or subtraction besides the saving of chip area.

New exponential bidirectional associative memory

A new exponential bidirectional associative memory model is introduced. It offers an even better recall performance than the modified exponential bidirectional associative memory (MEBAM). Only a little complexity of realization is spent for the improvement.

Capacity analysis of the asymptotically stable multi-valued exponential bidirectional associative memory

The exponential bidirectional associative memory (eBAM) has been proposed and proved to be a stable and high capacity associative neural network. However, the intrinsic structure and the evolution functions of this network restrict the representation of patterns to be either bipolar or binary vectors. We consider the promising development of multi-valued systems and then design a multi-valued discrete eBAM (MV-eBAM). The multi-valued eBAM has been proved to be asymptotically stable under certain constraints. Although MV-eBAM is also verified to possess high capacity by thorough simulations, there are important characteristics to be explored, including the absolute lower bound of the radix, and the approximate capacity. In order to estimate the capacity of the MV-eBAM, a modified evolution equation is also proposed. Hence, an analytic solution is derived. Besides, a radix searching algorithm is presented such that the absolute lower bound of the radix for this MV-eBAM can be found.

The decision-making properties of discrete multiple exponential bidirectional associative memories

A method for modeling the learning of belief combination in evidential reasoning using a neural network is presented. A centralized network composed of multiple exponential bidirectional associative memories (eBAM's) sharing a single output array of neurons is proposed to process the uncertainty management of many pieces of evidence simultaneously. The stability of the proposed multiple eBAM network is proved. The sufficient condition to recall a stored pattern pair is discussed. Most important of all, a majority rule of decision making in presentation of multiple evidence is also found by the study of signal-noise-ratio of multiple eBAM network. A guaranteed stable state condition, i.e., the condition for the fastest recall of a pattern pair, is also studied. The result is coherent with the intuition of reasoning.

An analysis of high-capacity discrete exponential BAM

An exponential bidirectional associative memory (eBAM) using an exponential encoding scheme is discussed. It has a higher capacity for pattern pair storage than conventional BAMs. A new energy function is defined. The associative memory takes advantage of the exponential nonlinearity in the evolution equations such that the signal-to-noise ratio (SNR) is significantly increased. The energy of the eBAM decreases as the recall process proceeds, ensuring the stability of the system. The increase of SNR consequently enhances the capacity of the BAM. The capacity of the exponential BAM is estimated.

Modified exponential bidirectional associative memories

Based on the Jeng exponential bidirectional associative memory (EBAM), a modified updating rule of EBAM is presented. In the recalling processes of the modified EBAM (MEBAM), the continuity assumption of the EBAM is relaxed and heterocorrelation processes run parallel with the autocorrelation processes. An energy function, which is defined and does not increase on the change of neuron states, ensures the stability of the system. Finally computer simulations demonstrates that the MEBAM has much better storage capacity than that of the Jeng EBAM.

Exponential bidirectional associative memories

A bidirectional associative memory (BAM) with an exponential nonlinearity is presented. This method has the advantage of exponential strategy. It improves the performance of the BAM. An energy function that decreases as the memory state changes can be defined, ensuring the stability of the system.