Computer Artificial Neural Networks Research Articles

A Survey of Artificial Neural Network Computing Systems

A Survey of Artificial Neural Network Computing Systems

Pure ZrO2 Ferroelectric Thin Film for Nonvolatile Memory and Neural Network Computing.

The recent discovery of ferroelectricity in pure ZrO2 has drawn much attention, but the information storage and processing performances of ferroelectric ZrO2-based nonvolatile devices remain open for further exploration. Here, a ZrO2 (∼8 nm)-based ferroelectric capacitor using RuO2 oxide electrodes is fabricated, and the ferroelectric orthorhombic phase evolution under electric field cycling is studied. A ferroelectric remnant polarization (2Pr) of >30 μC/cm2, leakage current density of ∼2.79 × 10-8 A/cm2 at 1 MV/cm, and estimated polarization retention of >10 years are achieved. When the ferroelectric capacitor is connected with a transistor, a memory window of ∼0.8 V and eight distinct states can be obtained in such a ferroelectric field-effect transistor (FeFET). Through the conductance manipulation of the FeFET, a high object image recognition accuracy of ∼93.32% is achieved on the basis of the CIFAR-10 dataset in the convolutional neural network (CNN) simulation, which is close to the result of ∼94.20% obtained by floating-point-based CNN software. These results demonstrate the potential of ferroelectric ZrO2 devices for nonvolatile memory and artificial neural network computing.

Predictive modelling of flow dynamics in micropolar hybrid nanofluids subjected to magnetic dipole influence using nonlinear autoregressive artificial neural networks with exogenous input

In the present research article, we explore the intelligence-based numerical computation of a nonlinear autoregressive artificial neural network with exogenous input using the Levenberg-Marquardt algorithm (ANN-LMA) trained to analyze the unsteady two-dimensional micro-polar flow of an incompressible hybrid nanofluid (Al 2 O 3−Fe 3 O 4/Ethylene Glycol) under the impact of the magnetic field introduced by the magnetic dipole on a nonlinear stretched sheet. In the first step of the research, partial differential equations are converted into nonlinear ordinary differential equations using appropriate transmission and then solved numerically via the Successive Over Relaxation method after applying the finite difference method. The impact of numerous emerging parameters on the solutions is displayed graphically, and the physical significance is discussed. The results show that the concentration profile displays a dwindling trend for the Brownian motion parameter, and the opposite trend is witnessed for the thermophoretic parameter. Moreover, for increasing values of the dimensionless distance parameter and ferromagnetic interaction parameter, the temperature and velocity profiles exhibit the opposite tendency. In the second step of the study, we validated the Successive Over Relaxation outcomes with an ANN-LMA. For this purpose, the suggested ANN-LMA is trained for six different scenarios of the reference dataset points of the given flow model. The analysis shows that Successive Over Relaxation and LM-SNNs yield similar results, with a decreasing trend for the Brownian motion parameter and an opposite trend for the thermophoretic parameter.

High-Reliability and Self-Rectifying Alkali Ion Memristor through Bottom Electrode Design and Dopant Incorporation.

Ionic memristor devices are crucial for efficient artificial neural network computations in neuromorphic hardware. They excel in multi-bit implementation but face challenges like device reliability and sneak currents in crossbar array architecture (CAA). Interface-type ionic memristors offer low variation, self-rectification, and no forming process, making them suitable for CAA. However, they suffer from slow weight updates and poor retention and endurance. To address these issues, the study demonstrated an alkali ion self-rectifying memristor with an alkali metal reservoir formed by a bottom electrode design. By adopting Li metal as the adhesion layer of the bottom electrode, an alkali ion reservoir was formed at the bottom of the memristor layer by diffusion occurring during the atomic layer deposition process for the Na:TiO2 memristor layer. In addition, Al dopant was used to improve the retention characteristics by suppressing the diffusion of alkali cations. In the memristor device with optimized Al doping, retention characteristics of more than 20 h at 125 °C, endurance characteristics of more than 5.5 × 105, and high linearity/symmetry of weight update characteristics were achieved. In reliability tests on 100 randomly selected devices from a 32 × 32 CAA device, device-to-device and cycle-to-cycle variations showed low variation values within 81% and 8%, respectively.

Retracted: Analysis of Comprehensive Artificial Neural Network Computer Media Aided Construction of Economic Forecasting Model

Retracted: Analysis of Comprehensive Artificial Neural Network Computer Media Aided Construction of Economic Forecasting Model

Learning-Aided Evolution for Optimization

Learning and optimization are the two essential abilities of human beings for problem solving. Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate the learning ability and the optimization ability for solving real-world problems, respectively. These have been two essential branches in artificial intelligence (AI) and computer science. However, in humans, learning and optimization are usually integrated together for problem solving. Therefore, how to efficiently integrate these two abilities together to develop powerful AI remains a significant but challenging issue. Motivated by this, this paper proposes a novel learning-aided evolutionary optimization (LEO) framework that plus learning and evolution for solving optimization problems. The LEO is integrated with the evolution knowledge learned by ANN from the evolution process of EC to promote optimization efficiency. The LEO framework is applied to both classical EC algorithms and some state-of-the-art EC algorithms including a champion algorithm, with benchmarking against the IEEE Congress on Evolutionary Computation competition data. The experimental results show that the LEO can significantly enhance the existing EC algorithms to better solve both single-objective and multi-/many-objective global optimization problems, suggesting that learning plus evolution is more intelligent for problem solving. Moreover, the experimental results have also validated the time efficiency of the LEO, where the additional time cost for using LEO is greatly deserved. Therefore, the promising LEO can lead to a new and more efficient paradigm for EC algorithms to solve global optimization problems by plus learning and evolution.

Neural network execution using nicked DNA and microfluidics.

DNA has been discussed as a potential medium for data storage. Potentially it could be denser, could consume less energy, and could be more durable than conventional storage media such as hard drives, solid-state storage, and optical media. However, performing computations on the data stored in DNA is a largely unexplored challenge. This paper proposes an integrated circuit (IC) based on microfluidics that can perform complex operations such as artificial neural network (ANN) computation on data stored in DNA. We envision such a system to be suitable for highly dense, throughput-demanding bio-compatible applications such as an intelligent Organ-on-Chip or other biomedical applications that may not be latency-critical. It computes entirely in the molecular domain without converting data to electrical form, making it a form of in-memory computing on DNA. The computation is achieved by topologically modifying DNA strands through the use of enzymes called nickases. A novel scheme is proposed for representing data stochastically through the concentration of the DNA molecules that are nicked at specific sites. The paper provides details of the biochemical design, as well as the design, layout, and operation of the microfluidics device. Benchmarks are reported on the performance of neural network computation.

Enhanced synaptic characteristics of H x WO3-based neuromorphic devices, achieved by current pulse control, for artificial neural networks

Artificial synapses capable of mimicking the fundamental functionalities of biological synapses are critical to the building of efficient neuromorphic systems. We have developed a H x WO3-based artificial synapse that replicates such synaptic functionalities via an all-solid-state redox transistor mechanism. The subject synaptic-H x WO3 transistor, which operates by current pulse control, exhibits excellent synaptic properties including good linearity, low update variation and conductance modulation characteristics. We investigated the performance of the device under various operating conditions, and the impact of the characteristics of the device on artificial neural network computing. Although the subject synaptic-H x WO3 transistor showed an insufficient recognition accuracy of 66% for a handwritten digit recognition task with voltage pulse control, it achieved an excellent accuracy of 88% with current pulse control, which is approaching the 93% accuracy of an ideal synaptic device. This result suggests that the performance of any redox-transistor-type artificial synapse can be dramatically improved by current pulse control, which in turn paves the way for further exploration and the evolution of advanced neuromorphic systems, with the potential to revolutionize the artificial intelligence domain. It further marks a significant stride towards the realization of high-performance, low-power consumption computing devices.

Research on Quantitative Model of Brand Recognition Based on Sentiment Analysis of Big Data.

This paper takes laptops as an example to carry out research on quantitative model of brand recognition based on sentiment analysis of big data. The basic idea is to use web crawler technology to obtain the most authentic and direct information of different laptop brands from first-line consumers from public spaces such as buyer reviews of major e-commerce platforms, including review time, text reviews, satisfaction ratings and relevant user information, etc., and then analyzes consumers’ sentimental tendencies and recognition status of the product brands. This study extracted a total of 437,815 user reviews of laptops from e-commerce platforms from January 1, 2019 to December 31, 2021, and performed data preprocessing on the obtained review data, followed by sentiment dictionary construction, attribute expansion, text quantification and algorithm evaluation. This paper analyzed the information receiving and processing hierarchy of the quantitative model of brand recognition, discussed the interactive relationship between brand recognition and consumer sentiment, discussed the brand recognition bias, style and demand in the context of big data, and performed the sentiment statistics and dimension analysis in the quantitative model of brand recognition. The study results show that the quantitative model of brand recognition based on sentiment analysis of big data can transform and map the keywords in text to word vectors in the high-dimensional semantic space by performing unsupervised machine learning on the text based on artificial neural network computer bionic metaphors; the model can accumulate each brand-related buyer review in the corresponding brand recognition dimension, so as to obtain the value of each product in each dimension of brand recognition; finally, the model will add the values of each dimension of brand recognition, that is, obtain the relevant value of the sum of each brand recognition. The results of this paper may provide a reference for further research on the quantitative model of brand recognition based on sentiment analysis of big data.

Human Language Understanding & Reasoning

Abstract The last decade has yielded dramatic and quite surprising breakthroughs in natural language processing through the use of simple artificial neural network computations, replicated on a very large scale and trained over exceedingly large amounts of data. The resulting pretrained language models, such as BERT and GPT-3, have provided a powerful universal language understanding and generation base, which can easily be adapted to many understanding, writing, and reasoning tasks. These models show the first inklings of a more general form of artificial intelligence, which may lead to powerful foundation models in domains of sensory experience beyond just language.

Analysis of Comprehensive Artificial Neural Network Computer Media Aided Construction of Economic Forecasting Model

As a complex system engineering, economic forecasting involves many fields and disciplines, and its accuracy largely depends on the selected forecasting method and forecasting model. The construction of the traditional economic prediction model mainly depends on the theories of economic statistics and metrology, which cannot meet the prediction needs of the new era. ANN model has a more flexible nonlinear quality and learning mode so that it can effectively adapt to the prediction scenario of a nonlinear economic system. ANN is an abstract model of the human brain neuronal network based on the information-processing perspective and composed of different networks to achieve a specific representation of logical strategies. ANN’s self-learning function, associative storage function, and the ability to find optimal solutions at high speed have led to its in-depth application and research in an increasing number of fields. In addition, ANN research work has continued to advance, has made great progress in many fields represented by economic forecasting, has successfully solved many practical problems, showing a more intelligent adaptation in the field of economics, and therefore has an important research value. Based on this, this paper first analyzes the economic system prediction, then studies the ANN medium model and learning algorithm, and finally verifies the effectiveness of the ANN economic prediction model integrating computer medium.

Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing

The purpose is to better develop China's music industry, facilitate users' online music works queries, and encourage the development of excellent music works. The music recommendation system is analysed, and a new music recommendation system is constructed based on the combination of the hybrid Deep Learning (DL) Artificial Neural Network (ANN) and Mobile Edge Computing (MEC) technologies. First, the principle of MEC is analysed. Then, the combination of Deep Learning Artificial Neural Network (DLANN) technology and MEC technology is discussed. Afterward, DLANN and MEC are fused to implement the music recommendation model, and the proposed model is evaluated through experiment. The results show that the combination of DLANN and MEC technologies can increase the DL efficiency of the computer, the storage capacity, and the overall efficiency of the servers. This proves the feasibility of the proposed music recommendation system. The user satisfaction of the proposed music recommendation system exceeds all mainstream music recommendation algorithms on the market. Thus, the research can provide a reference for the improvement of the music recommendation algorithm in the future and is of great significance. The proposed music recommendation system outperforms most of the current music recommendation systems on the market.

РАЗРАБОТКА МОДЕЛИ ПРОГНОЗИРОВАНИЯ ТЕХНИЧЕСКОГО СОСТОЯНИЯ НЕФТЕПРОМЫСЛОВОГО ЭНЕРГОМЕХАНИЧЕСКОГО ОБОРУДОВАНИЯ С ПРИМЕНЕНИЕМ ИСКУССТВЕННОЙ НЕЙРОННОЙ СЕТИ

Relevance The work is devoted to the development of a system for predicting the technical condition of oilfield power-mechanical equipment, in particular the installation of an electric centrifugal pump, as a way to solve the problem of failure of electrical equipment associated with the exhaustion of its resource. Aims of research Improving the reliability of oilfield power-mechanical equipment by predicting the technical condition of the installation of an electric centrifugal pump in the current operating conditions. Research methods To determine the technical condition of the installation of an electric centrifugal pump, artificial neural network methods and computer applications were used. Results A method for implementing an artificial neural network is presented, which makes it possible to predict a change in the technical state of electrical equipment during further operation under current conditions. A schedule for predicting the technical condition of the ESP has been obtained.

Moving beyond content‐specific computation in artificial neural networks

A basic deep neural network (DNN) is trained to exhibit a large set of input–output dispositions. While being a good model of the way humans perform some tasks automatically, without deliberative reasoning, more is needed to approach human‐like artificial intelligence. Analysing recent additions brings to light a distinction between two fundamentally different styles of computation: content‐specific and non‐content‐specific computation (as first defined here). For example, deep episodic RL networks draw on both. So does human conceptual reasoning. Combining the two takes advantage of the complementary costs and benefits of each. It also offers a better model of human cognitive competence.

Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer

In the presented research article, the intelligence based numerical computation of artificial neural network backpropagated with Levenberg-Marquardt algorithm has been developed to analyze the novel ferrofluid flow model in the presence of magnetic dipole. Heat transfer effects are also incorporated along the horizontal. The designed fluid flow model initially represented by system of partial differential equations are converted into system of non-linear ordinary differential equations through suitable similarity transformations. The reference dataset of the possible outcomes is obtained from Adam numerical solver for the different scenarios of flow model by variation of co-efficient of the thermal expansion, Eckert number, suction parameter, magnetization and radiation parameter. The approximated solutions are interpreted for designed model by testing, training and validation process of backpropagated neural networks. Furthermore, the comparative studies and performance analysis of used algorithm is validated through regression analysis, histogram studies, correlation index and results of mean square error.

Warehouse Management Models Using Artificial Intelligence Technology with Application at Receiving Stage – A Review

This paper reviewed recent literature on inventory management technologies and Artificial Intelligence (AI) applications. The classical Artificial Neural Network (ANN) models and computer vision technology applications for object classification were reviewed in particularly. The challenges of AI technologies in industrial warehouse management, particularly the ANN for solving object classification and counting are discussed. Some researchers reported the use of face recognition, moving vehicle classification and counting, which are easy to recognise objects on the floor or the ground. Other researchers explored the object counting technologies which are used to identify the visible objects on the ground or in images. Although several studies focused on industrial component identification and counting problems, a study on the warehouse receiving stage remains a blank canvas. This paper reviews and analyses current industrial warehouse management developments around AI applications in this field, which may provide a reference for future researchers and end-users for the best modelling approach to this specific problem at the warehouse receiving stage.

Matrix-Based Evolutionary Computation

Computational intelligence (CI), including artificial neural network, fuzzy logic, and evolutionary computation (EC), has rapidly developed nowadays. Especially, EC is a kind of algorithm for knowledge creation and problem solving, playing a significant role in CI and artificial intelligence (AI). However, traditional EC algorithms have faced great challenge of heavy computational burden and long running time in large-scale (e.g., with many variables) problems. How to efficiently extend EC algorithms to solve complex problems has become one of the most significant research topics in CI and AI communities. To this aim, this paper proposes a matrix-based EC (MEC) framework to extend traditional EC algorithms for efficiently solving large-scale or super large-scale optimization problems. The proposed framework is an entirely new perspective on EC algorithm, from the solution representation to the evolutionary operators. In this framework, the whole population (containing a set of individuals) is defined as a matrix, where a row stands for an individual and a column stands for a dimension (decision variable). This way, the parallel computing functionalities of matrix can be directly and easily carried out on the high performance computing resources to accelerate the computational speed of evolutionary operators. This paper gives two typical examples of MEC algorithms, named matrix-based genetic algorithm and matrix-based particle swarm optimization. Their matrix-based solution representations are presented and the evolutionary operators based on the matrix are described. Moreover, the time complexity is analyzed and the experiments are conducted to show that these MEC algorithms are efficient in reducing the computational time on large scale of decision variables. The MEC is a promising way to extend EC to complex optimization problems in big data environment, leading to a new research direction in CI and AI.

Parametric investigation of the effects of load level on fatigue crack growth in trabecular bone based on artificial neural network computation.

This study reports the development of an artificial neural network computation model to predict the accumulation of crack density and crack length in cancellous bone under a cyclic load. The model was then applied to conduct a parametric investigation into the effects of load level on fatigue crack accumulation in cancellous bone. The method was built in three steps: (1) conducting finite element simulations to predict fatigue growth of different three-dimensional micro-computed tomography cancellous bone specimens considering input combinations based on a factorial experimental design; (2) performing a training stage of an artificial neural network based on the results of step 1; and (3) applying the trained artificial neural network to rapidly predict the crack density and the crack length growth for cancellous bone under a cyclic loading for a given applied apparent strain, cycle frequency, bone volume fraction, bone density and apparent elastic modulus.

Characterizing Computation in Artificial Neural Networks by their Diclique Covers and Forman-Ricci Curvatures

The relationships between the structural topology of artificial neural networks, their computational flow, and their performance is not well understood. Consequently, a unifying mathematical framework that describes computational performance in terms of their underlying structure does not exist. This paper makes a modest contribution to understanding the structure-computational flow relationship in artificial neural networks from the perspective of the dicliques that cover the structure of an artificial neural network and the Forman-Ricci curvature of an artificial neural network’s connections. Special diclique cover digraph representations of artificial neural networks useful for network analysis are introduced and it is shown that such covers generate semigroups that provide algebraic representations of neural network connectivity.

Characterizing Computation in Artificial Neural Networks by their Diclique Covers and Forman-Ricci Curvatures

The relationships between the structural topology of artificial neural networks, their computational flow, and their performance is not well understood. Consequently, a unifying mathematical framework that describes computational performance in terms of their underlying structure does not exist. This paper makes a modest contribution to understanding the structure-computational flow relationship in artificial neural networks from the perspective of the dicliques that cover the structure of an artificial neural network and the Forman-Ricci curvature of an artificial neural network’s connections. Special diclique cover digraph representations of artificial neural networks useful for network analysis are introduced and it is shown that such covers generate semigroups that provide algebraic representations of neural network connectivity.