Analysis of Japanese Logistics Development Mode Based on Neural Network and Computer Simulation

To understand the mechanism of information processing by a biological neural network, computer simulation of a large-scale spiking neural network is an important method. However, because of a high computation cost of the simulation of a large-scale spiking neural network, the simulation requires high performance computing implemented by a supercomputer or a computer cluster. Recently, hardware for parallel computing such as a multi-core CPU and a graphics card with a graphics processing unit (GPU) is built in a gaming computer and a workstation. Thus, parallel computing using this hardware is becoming widespread, allowing us to obtain powerful computing power for simulation of a large-scale spiking neural network. However, it is not clear how much increased performance the parallel computing method using a new GPU yields in the simulation of a large-scale spiking neural network. In this study, we compared computation time between the computing methods with CPUs and GPUs in a simulation of neuronal models. We developed computer programs of neuronal simulations for the computing systems that consist of a gaming graphics card with new architecture (the NVIDIA GTX 1080) and an accelerator board using a GPU (the NVIDIA Tesla K20C). Our results show that the computing systems can perform a simulation of a large number of neurons faster than CPU-based systems. Furthermore, we investigated the accuracy of a simulation using single precision floating point. We show that the simulation results of single precision were accurate enough compared with those of double precision, but chaotic neuronal response calculated by a GPU using single precision is prominently different from that calculated by a CPU using double precision. Furthermore, the difference in chaotic dynamics appeared even if we used double precision of a GPU. In conclusion, the GPU-based computing system exhibits a higher computing performance than the CPU-based system, even if the GPU system includes data transfer from a graphics card to host memory.

We here describe SNNS, a neural network simulator for Unix workstations that we have been developing at the Universitat Stuttgart, Germany. Our network simulation environment is a tool to generate, train, test and visualize artificial neural networks. The simulator consists of three major components: a simulator kernel that operates on the internal representation ofthe neural networks, a graphical user interface based on X-Windows to interactively construct and change small neural nets,:ind a compiler to generate the internal representation of large neural networks from a high level network description language.Keywords: connectionism, neural networks, network simulators, network description language 1. INTRODUCTION AND MOTIVATION SNNS (Stuttgart Neural Network Simulator) is an efficient and portable neural network simulation environment for Unixworkstations that we have been developing at the Institute for Parallel and Distributed High Performance Systems, Universitht Stuttgart, Germany. It is an efficient software tool to generate, train, test and visualize artificial neural networks. The

High-performance fault simulation is one of the essential and preliminary tasks in the process of online and offline testing of machine learning (ML) hardware. Deep neural networks (DNN), as one of the essential parts of ML programs, are widely used in many critical and non-critical applications in Systems-on-Chip and ASIC designs. Through fault simulation for DNNs, by increasing the number of neurons, the fault simulation time increases exponentially. However, the software architecture of neural networks and the lack of dependency between neurons in each inference layer provide significant opportunity for parallelism of the fault simulation time in a multi-processor platform. In this paper, a multi-thread technique for hierarchical fault simulation of neural network is proposed, targeting both permanent and transient faults. During the process of fault simulation the neurons for each inference layer will be distributed among the executing threads. Since in the process of hierarchical fault simulation, the faulty neuron demands proportionally enormous computation comparing to behavioural model of non-faulty neurons, the faulty neuron will be assigned to one thread while the rest of the neurons will be divided among the remaining threads. Experimental results confirm the time efficiency of the proposed fault simulation technique on multi-processor architectures.

Neural networks provide the opportunity to estimate specific components of engineering problems. They are decomposed complex problems into different parts. Thus, it can be easy to compete with each of them through neural networks. In this paper, it was purposed to estimate the average speed of a 6-line road’s cross-section by observed traffic variables, such as numbers of vehicles and occupancy values, using radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and the feed-forward back propagation neural network (FFBPNN) models. A comparison was fulfilled between different neural networks and checked against multivariate linear regression (MVLR), a conventional statistical model. After each simulation of neural networks, results show that different forecasts were obtained under the same conditions. The best forecasting is made by FFBPNN, GRNN, and RBFNN, respectively. When compared with multivariate linear regression (MVLR), FFBPNN performs better than MVLR, but GRNN and RBFNN perform lower than it.

This paper analyzes, theoretically and by computer simulation, the capabilities and limitations of neural networks to be used for the identification and optimal control of nonlinear dynamical systems. It is shown how neural networks can be efficiently trained to identify the forward and inverse dynamics of systems and also to work as optimal controllers which minimize some peformance measures of the system to be controlled. The performance of neural networks when applied to the optimal control of nonlinear vehicle suspensions is analyzed and compared with the performance of passive suspensions and that of active suspensions with linear (LQ) controllers designed by linearizing the nonlinear characteristics of the suspensions around the equilibrium point. It is found that nonlinear vehicle suspensions with neuro-control show better performance than suspensions controlled with conventional LQ regulators. Some issues about the implementation of neural networks to improve its convergence and generalization properties are analyzed.

THE PYGMALION NEURAL NETWORK PROGRAMMING ENVIRONMENT

The field of neural networks is being investigated by many researchers in order to provide solutions to difficult problems in the area of manufacturing systems. Computer simulation of neural networks is an important part of this investigation. This paper applies concepts from an important trend in software engineering research, namely object-oriented programming, to model neural networks.

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The application of neural network in cryptographic known plaintext attack is a novel cryptanalysis method. Only with known ciphertext, the restoration of plaintext is a valuable part of cryptanalysis. By putting plaintext and ciphertext into model, the network simulates encryption algorithm without the key and output the corresponding plaintext. In this paper, we focus on the plaintext restoration of Blowfish based on neural network. We design a plaintext restoration scheme and perform the experiment in open source database. The result shows that the neural network simulator could fit the dataset with an error rate lower than 0.04 when the size of dataset exceeds 219•.

The skill of experienced log analysts is emulated by a new computer technology called Simulated Neural Networks (SNNs). Two SNNs are applied to problems in log interpretation. The first SNN is applied to a trivial and conceptually familiar interpretation problem: learning to compute total porosity (PHIT) and apparent grain density (RHOMA) from log data and lithology inputs. The second SNN learned to identify lithofacies patterns as a response to input log data. Interesting and human-like results were obtained when these SNNs interpreted log data in a well which has been full bore cored from top to bottom. Both SNNs performed as well as experienced humans and at least as well or better than conventional sequential computer program interpretations of the same two analysis problems.

The major bottleneck in simulation of large-scale neural networks is the communication problem due to one-to-many neuron connectivity. Network-on-Chip concept has been proposed to address the problem. This work explores the drawback that is introduced by interconnection networks - a delay jitter. The preliminary experiment is held in the spiking neural network simulator introducing variable communicational delay to the simulation. The performance degradation is reported.

The research technique of human motor nerve is more complicated. In order to improve the understanding level of human motor nerve structure, a fusion architecture of human motion nerve and neural network computer driven by education are proposed. The human motor nerve simulation model is established by using the computer data simulation model. The model proposes an optimization scheme from the algorithm flow, the data transformation technology used to improve the real-time performance of the neural network and enhance the dynamic capture of the motor nerve changes. In order to further improve the architecture of the neural network and computer architecture driven by sports, a neural network algorithm is added to realize the sequence optimization of data to test the authenticity and efficiency of the human neural network simulation model neural network algorithm. In this paper, the feasibility and functionality of the algorithm are tested with comparative experiments. The results show that the neural network algorithm neural network algorithm not only has a calculation time of only 12 seconds. Moreover, the calculation accuracy is high, achieving a high level of accuracy of 98%. On the other hand, other algorithms have lower accuracy and longer calculation time. The research shows that the neural network algorithm can improve the human motor nerve capture and optimize the architecture, which can provide reference for the fusion of human motion nerve and computer technology in the future.

Simulation of an artificial neural network for predicting temperature and cutting force during grinding using CAMQL

NeuroGraph is a simulation environment for design, construction and execution of neural networks and genetic algorithms in a distributed computing environment. The simulator parts either run on single computers or as distributed applications on UNIX/X-based networks consisting of personal computers, workstations or multi-processors. The parallelization component offers the possibility to divide computational tasks into concurrently executable modules, according to restrictions due to the neural net topology and computer net capabilities, i.e. NeuroGraph tries to select the best configuration out of the available distributed hardware environment to fit performance requirements.

In today's technology based society the rapid proliferation of new machines and systems that would have been undreamed of only a few short years ago has become a way of life.Developments and advances especially in the areas of digital electronics and micro-circuitry have spawned subsequent technology based improvements in transportation, communications, entertainment, automation, the armed forces, and many other areas that would not have been possible otherwise.This rapid "explosion" of new capabilities and ways of performing tasks has been motivated as often as not by the philosophy that if it is possible to make something better or work faster or be more cost effective or operate over greater distances then it must inherently be good for the human operator.Taken further, these improvements typically are envisioned to consequently produce a more efficient operating system where the human operator is an integral component.The formal concept of human-system interface design has only emerged this century as a recognized academic discipline, however, the practice of developing ideas and concepts for systems containing human operators has been in existence since humans started experiencing cognitive thought.An example of a human system interface technology for communication and dissemination of written information that has evolved over centuries of trial and error development, is the book.It is no accident that the form and shape of the book of today is as it is.This is because it is a shape and form readily usable by human physiology whose optimal configuration was determined by centuries of effort and revision.This slow evolution was mirrored by a rate of technical evolution in printing and elsewhere that allowed new advances to be experimented with as part of the overall use requirement and need for the existence of the printed word and some way to contain it.Today, however, technology is advancing at such a rapid rate that evolutionary use requirements have no chance to develop along side the fast pace of technical progress.One result of this recognition is the establishment of disciplines like human factors engineering that have stated purposes and goals of systematic determination of good and bad human system interface designs.However, other results of this phenomenon are systems that get developed and placed into public use simply because new technology allowed them to be made.This development can proceed without a full appreciation of how the system might be used and, perhaps even more significantly, what impact the use of this new system might have on the operator within it.The U.S. Army has a term for this type of activity.It is called "stove-piped development."The implication of this term is that a system gets developed in isolation where the developers are only looking "up" and not "around."They are thus concerned only with how this system may work or be used for its own singular purposes as opposed to how it might be Dr.Robert J. Beaton has added a wealth of knowledge in workload and system design.Dr. C. Patrick Koelling has opened my eyes to the real potential and capabilities of computer simulation analysis even though I thought I was a world class expert in that field when I came here.Dr. Tonya L.