A neuro-emulator with embedded capabilities for generalized learning

The considerable improvement of technology produced for various applications has resulted in a growth in data sizes, such as healthcare data, which is renowned for having a large number of variables and data samples. Artificial neural networks (ANN) have demonstrated adaptability and effectiveness in classification, regression, and function approximation tasks. ANN is used extensively in function approximation, prediction, and classification. Irrespective of the task, ANN learns from the data by adjusting the edge weights to minimize the error between the actual and predicted values. Back Propagation is the most frequent learning technique that is used to learn the weights of ANN. However, this approach is prone to the problem of sluggish convergence, which is especially problematic in the case of Big Data. In this paper, we propose a Distributed Genetic Algorithm based ANN Learning Algorithm for addressing challenges associated with ANN learning for Big data. Genetic Algorithm is one of the well-utilized bio-inspired combinatorial optimization methods. Also, it is possible to parallelize it at multiple stages, and this may be done in an extremely effective manner for the distributed learning process. The proposed model is tested with various datasets to evaluate its realizability and efficiency. The results obtained from the experiments show that after a specific volume of data, the proposed learning method outperformed the traditional methods in terms of convergence time and accuracy. The proposed model outperformed the traditional model by almost 80% improvement in computational time.

A VLSI bio-inspired microsystem for real-time, on-site, robust image analysis and recognition has been developed. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized.

Optimal learning in artificial neural networks: A review of theoretical results

Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic hybrid intelligent system. It combines the human-like reasoning style of fuzzy logic system (FLS) with the learning and computational capabilities of artificial neural networks (ANNs). ANFIS has several applications related to food processing and technology. The first part of this review provides a brief overview and discussion of ANFIS including: the general structure and topology, computational considerations, model development and testing. In the second part, two detailed examples are explained to demonstrate the capabilities of ANFIS in comparison with other modeling methods, followed by a brief but comprehensive discussion of ANFIS applications in different food processing and technology areas. The applications are divided into five main categories: food drying, prediction of food properties, microbial growth and thermal process modeling, applications in food quality control and food rheology. In all applications, the performance of ANFIS is compared to other methods such as ANNs, FLS and multiple regressions when available. It is concluded that, in most applications, ANFIS outperforms other modeling tools such as ANNs, FIS or multiple linear regression. Finally, some application guidelines, advantages and disadvantages of ANFIS are discussed.

To manage the inventory efficiently, it is necessary to have accurate forecasting. To extract and deploy the knowledge associated with forecasting attracts the attention of both academic and practitioners. Knowledge is regarded as a valuable asset for enterprises and it can be manipulated through intelligence techniques like Artificial Neural Networks (ANN). ANN has the special ability to learn facts about one knowledge domain by inputting data obtained from observations. This study focuses on exploring how ANN learns and analyzes different types of ANN and ANN architectures used in the demand forecasting. The feasibility of the proposed approach to the demand forecasting issue is demonstrated with numeric data. The significance of this study is to adopt ANN as a knowledge discovery system thereby enhancing the inventory management.

AFTER MORE THAN TWO DECADES OF NEGLECT, artificial neural networks (ANNs) are emerging as a strong contender in artificial intelligence. However, researchers whose leanings are more toward expert systems or other types of symbolic formalisms for knowledge representations warn against overly enthusiastic claims depicting ANNs as replicating the process of human thinking. While some of these objections are justified, the technology warrants a closer look by designers of educational software. Though neural networks are usually considered as a part of artificial intelligence, they are very different from the AI formalisms to which we’re accustomed. Expert systems use if-then rules to arrive at an answer. These rules are formulated by a knowledge engineer who works closely with a content expert to derive a model of the problem space. Neural networks, however, are fed sample cases (called “training sets≓), learn how to recognize patterns in the sample data, and then use this experience in handling new cases. Because neural nets are trained rather than programmed, the only overhead — other than the initial design — is in collecting and formatting the data sets (Carlson, 1990b, p. 50). This paper first describes the essentials of how an ANN learns, using a classic application in pattern recognition to demonstrate an operating ANN. Of the several “cognitive≓ roles ANNs are suitable for, two seem especially appropriate for computer-mediated education: (1) intelligent filters and (2) spreading, associative memories. To illustrate each, I describe prototypes I’m working on at the Air Force Armstrong Laboratory.

The Levenberg-Marquardt and Newton are two algorithms that use the Hessian for the artificial neural network learning. In this article, we propose a modified Levenberg-Marquardt algorithm for the artificial neural network learning containing the training and testing stages. The modified Levenberg-Marquardt algorithm is based on the Levenberg-Marquardt and Newton algorithms but with the following two differences to assure the error stability and weights boundedness: 1) there is a singularity point in the learning rates of the Levenberg-Marquardt and Newton algorithms, while there is not a singularity point in the learning rate of the modified Levenberg-Marquardt algorithm and 2) the Levenberg-Marquardt and Newton algorithms have three different learning rates, while the modified Levenberg-Marquardt algorithm only has one learning rate. The error stability and weights boundedness of the modified Levenberg-Marquardt algorithm are assured based on the Lyapunov technique. We compare the artificial neural network learning with the modified Levenberg-Marquardt, Levenberg-Marquardt, Newton, and stable gradient algorithms for the learning of the electric and brain signals data set.

A Novel Weights of Weights Approach for Efficient Transfer Learning in Artificial Neural Networks

A microsystem architecture for real-time, on-site, robust bioinformatic patterns recognition and analysis has been proposed. This system is compatible with on-chip DNA analysis means such as polymerase chain reaction (PCR) amplification. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function based on error backpropagation (EBP) algorithm is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized

In this article, we describe a visualization tool for teaching and learning the basics of artificial neural networks with a user interface and a mobile robot. The participants are given a brief introduction to artificial neural networks and are introduced to the visualization tool. Participants had different educational backgrounds including Computer Science, Information Technology, Language and Translation, Law and Psychology. Most of the participants were excited to be a part of the study and they were able to relate the artificial neural networks to real-life applications. The study suggests that visualization tools can increase interest in teaching and learning certain topics regardless of its complexity. It also suggests that similar tools can be utilized in teaching topics related to Artificial Intelligence.

Key aspects of the categorical perception of initial stop consonants by human and animal listeners are replicated by a ‘‘synthetic listener’’ in the form of a computational auditory model. This model is a composite of a physiologically faithful ‘‘front end’’ [M. J. Pont and R. I. Damper, J. Acoust. Soc. Am. 89, 1213–1228 (1991)] and an artificial neural network (ANN) ‘‘back end.’’ ANNs are trained on neurograms (i.e., the front end’s auditory-nerve representation) for the 0- and 80-ms end points of three voice-onset time continua (bilabial, alveolar, and velar), and tested on the intermediate (10, 20, ..., 70 ms) patterns. Replication of the behavioral data—in terms of warping of similarity space and shift of phoneme boundary with place of articulation—is robust in that it is maintained across a variety of ANN architectures and learning algorithms. Unlike real listeners, the synthetic listener can be analyzed easily and manipulated purposefully to reveal the acoustic and auditory features underlying the voicing contrast. A contribution analysis of the weights and activations of the ANNs highlights the auditory features. Replacement of the Pont–Damper front end by a simple Fourier-based spectral analysis abolishes the boundary-shift effect, although similarity warping is maintained. This suggests that restructuring of the acoustic features by the peripheral auditory system is crucial to phonetic classification.

This paper introduces a novel architecture-bidirectional optical neural network (BONN)-for providing backward connections alongside forward connections in artificial neural networks (ANNs). BONN incorporates laser diodes and photodiodes and exploits the properties of Köhler illumination to establish optical channels for backward directions. Thus, it has bidirectional functionality that is crucial for algorithms such as the backpropagation algorithm. BONN has a scaling limit of 96 × 96 for input and output arrays, and a throughput of 8.5 × 1015 MAC/s. While BONN's throughput may rise with additional layers for continuous input, limitations emerge in the backpropagation algorithm, as its throughput does not scale with layer count. The successful BONN-based implementation of the backpropagation algorithm requires the development of a fast spatial light modulator to accommodate frequent data flow changes. A two-mirror-like BONN and its cascaded extension are alternatives for multilayer emulation, and they help save hardware space and increase the parallel throughput for inference. An investigation into the application of the clustering technique to BONN revealed its potential to help overcome scaling limits and to provide full interconnections for backward directions between doubled input and output ports. BONN's bidirectional nature holds promise for enhancing supervised learning in ANNs and increasing hardware compactness.


 
 
 
 One of the weakness in backpropagation Artificial neural network(ANN) is being stuck in local minima. Learning rate parameter is an important parameter in order to determine how fast the ANN Learning. This research is conducted to determine a method of finding the value of learning rate parameter using a genetic algorithm when neural network learning stops and the error value is not reached the stopping criteria or has not reached the convergence. Genetic algorithm is used to determine the value of learning rate used is based on the calculation of the fitness function with the input of the ANN weights, gradient error, and bias. The calculation of the fitness function will produce an error value of each learning rate which represents each candidate solutions or individual genetic algorithms. Each individual is determined by sum of squared error value. One with the smallest SSE is the best individual. The value of learning rate has chosen will be used to continue learning so that it can lower the value of the error or speed up the learning towards convergence.
 
 
 
 
 The final result of this study is to provide a new solution to resolve the problem in the backpropagation learning that often have problems in determining the learning parameters. These results indicate that the method of genetic algorithms can provide a solution for backpropagation learning in order to decrease the value of SSE when learning of ANN has been static in large error conditions, or stuck in local minima

The importance of long-range prediction of rainfall pattern for devising and planning agriculture strategies cannot be overemphasized. Prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. In this paper, a committee of artificial neural networks (ANNs) based model with wavelet decomposition is proposed for prediction of monthly rainfall on accounts of the preceding events of rainfall data. Wavelet transform is used for extraction of approximate and detail coefficient of the rainfall data series. These coefficients are used along with a ANN for learning and knowledge extraction processes. The model has been tested on rainfall data for different geographical region of India and also for entire country. The proposed model is capable of forecasting monthly rainfall one month in advance

This paper presents an extension of an existing hybrid model for the development of knowledge-based systems, combining case-based reasoning (CBR) and artificial neural networks (ANN). The extension consists of the modeling of predictive attributes in terms of fuzzy sets. As such, representative values for numeric attributes are fuzzy sets, facilitating the use of natural language, thus accounting for words with ambiguous meanings. The topology and learning of the associative ANN are based on these representative values. The ANN is used for suggesting the value of the target attribute for a given query. Afterwards, the case-based module justifies the solution provided by the ANN using a similarity function, which includes the weights of the ANN and the membership degrees in the fuzzy sets considered. Experimental results show that the proposed model preserves the advantages of the hybridization used in the original model, while guaranteeing robustness and interpretability.