A tool for process identification and neural network simulation by means of a personal digital assistant

Artificial neural network (ANN) is a modern computing paradigm. That can be used for the pattern recognition and other. Backpropagation is artificial neural network which using hidden layer addition. Computation of artificial neural network through some certain step like training phase and examination. After both the step reached, so a neural network capable to recognize pattern to be entered will be found. The purpose of this research is simulation of artificial neural network that capable to pattern recognition from output of electrocardiogram by helped of MATLAB program. Input of result electrocardiogram record, then input of data can be normalization after that data can be proccesed by backpropagation computing with two step (training phase and examination phase). Output of ANN is like explaning condition of patient is normal, rhinitis kronis or epistaksis. Keyword : rhinitis kronis, epistaksis, artificial neural network.

The project presents change detection approach for synthetic aperture radar (SAR) images based on an image fusion and supervised classifier system. The image fusion technique will be introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. NSCT (Non- subsampled contourlet transform) fusion rules based on an average operator and minimum local area gradient are chosen to fuse the contourlet coefficients for a low-frequency band and a high-frequency band respectively, to restrain the background information and to enhance the information of changed regions in the fused difference image. For the remote sensing images, differencing(subtraction operator) and ratioing (ratio operator) are well-known techniques for producing a difference image. In differencing, changes are measured by subtracting the intensity values pixel by pixel between the considered couple of temporal images. In ratioing, changes are obtained by applying a pixel-by-pixel ratio operator to the considered couple of temporal images. In the case of SAR images, the ratio operator is typically used instead of the subtraction operator since the image differencing technique is not adapted to the statistics of SAR images. An artificial neural network type multi-layer perceptron or back propagation with feed forward network will be proposed for classifying changed and unchanged regions in the fused difference image. This classifier comes under supervised segmentation which is worked based on training cum classification. The results will be proven that ratioing generates better difference image for change detection, using supervised classifier segmentation approach and efficiency of this algorithm will be exhibited by sensitivity and correlation evaluation.

Research on artificial neural networks (ANNs) has been carried out for more than five decades. A renewed interest appeared in the 80's with the finding of powerful models like J. Hopfield's recurrent networks, T. Kohonen's self-organizing feature maps, and the back-propagation rule. At that time, there was no platform that was at the same time versatile enough for any ANN model to be implemented and fast enough to solve large problems. Super-computers were the sole exception to this rule, but were prohibitively expensive for most applications. However, both research scientists and application engineers clearly identified the need for such a computing power. This triggered many projects in the field. In parallel, research on multi-processor systems started during the 60's. Systolic arrays have been proposed in 1979 as a means to fully exploit the possibilities of VLSI. Two previous theses, by F. Blayo and C. Lehmann, have studied the use of bi-dimensional systolic arrays for neural computation. At first, the presented system, called GENES, has been designed for the Hopfield model. Extensions to other ANNs have also been proposed. The goal of the present thesis is to study, design, build, and analyze an efficient accelerator for neural computation. In a first step, the GENES architecture has been extended towards generality and efficiency. This includes a thorough analysis of ANN models, of other neural computers, and of previous GENES implementations. The result of this work is the GENES IV integrated circuit, whose architecture has been co-designed by P. Ienne and the author. The main part of this thesis discusses the architecture, the design and the analysis of the MANTRA I machine, a neural computer based on a GENES IV array with up to 40 × 40 processing elements (PEs). The delta rule (and hence the Perceptron and Adaline rules), the back-propagation rule, the Hopfield model, and the Kohonen model can be implemented on this system. Although not a generic system, such a machine may be regarded as a multi-model neural computer. A prototype has been running for a year and is used daily by software designers. Several novel features distinguish the MANTRA I machine from other neural systems. First, it belongs to the few existing neural computers, contrary to the majority of implementations, which are specific to an application or to an algorithm. The machine does not hard-wire any algorithm, but provides the necessary primitives to implement the target models. This is a key feature for research, since several algorithms or versions of an algorithm can be tested on a problem. It is an important aspect for applications as well, because different ANN models are often cascaded to solve a problem. The GENES IV array — that is, the computing core of the MANTRA I machine — features synapse-level parallelism (i.e., one real or virtual PE is allocated per synapse or neural connection), while most other systems exploit only neuron-level parallelism (i.e., one PE per neuron). Hence, this system aims at a much finer parallelism grain and is well suited for massively parallel architectures. The problem size that can be computed by a neural accelerator should not be limited by the hardware (except for memory size). Therefore, it is essential to support time-sharing of PEs. On the MANTRA I machine, this is achieved by the concept of virtual arrays. Matrices are divided into sub-matrices that can be mapped onto the physical array, which is then time-shared among them. An efficient mechanism has been implemented to swap sub-matrices in background, while some other computation is performed. Since systolic arrays are pipelined systems, it is important to avoid emptying and re-filling them too often, in order to keep the hardware utilization rate high. Therefore, a systolic instruction flow has been implemented, so that each instruction follows the data for which it has been issued. Like any SIMD system, the MANTRA I machine is composed of a parallel or SIMD module and a control module. The SIMD module consists of the GENES IV array and a set of dedicated units designed for the computation that scales with the number of neurons and would poorly fit on a bi-dimensional array. A complex system of FIFOs and memories sustains the required input/output streams for the systolic array. The control module is a complete SISD system. Its tasks are (1) to control the SIMD module by dispatching instructions, (2) to manage data input and output, (3) to communicate with the external world, and (4) to perform data pre- and post-processing. The SIMD instructions are of the very long instruction word (VLIW) type. Synchronization between the two modules is achieved by an instruction FIFO. The performance of the MANTRA I machine has been analyzed using the delta rule. Measurements show that the sustained performance is very close to its peak value, as long as the problem fits in the memory banks connected to the GENES IV array. Experiments have also been run to investigate the impact of the constraints imposed by the hardware on the convergence of algorithms. Finally, the use of systolic arrays as neural accelerators is discussed in the light of the experience acquired with the GENES IV array and the MANTRA I machine. The weaknesses of the machine are analyzed, and several solutions are proposed to avoid them in a future design. A general discussion of the future of neural computers concludes this thesis.

The paper describes the implementation of a systolic array for a multilayer perceptron on a Virtex XCV400 FPGA with a hardware-friendly learning algorithm. A pipelined adaptation of the on-line backpropagation algorithm is shown. Parallelism is better exploited because both forward and backward phases can be performed simultaneously. We can implement very large interconnection layers by using large Xilinx devices with embedded memories alongside the projection used in the systolic architecture. These physical and architectural features --- together with the combination of FPGA reconfiguration properties with a design flow based on generic VHDL --- create an easy, flexible, and fast method of designing a complete ANN on a single FPGA. The result offers a high degree of parallelism and fast performance.

An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer. The network was trained by a particle swarm optimization (PSO) algorithm. Nanofluid concentration, Reynolds number, and Prandtl number are input for the ANN and the nanofluid Nusselt number is its output. There exists an excellent agreement between the ANN predicted values and experimental data. The average and maximum differences between experimental data and those predicted by ANN are about 0.8 and 5.6 %, respectively. It was also found that ANN predicts the Nusselt number of nanofluids more accurately than the previously proposed correlation.

Biosorption of copper(II) ions by flax meal: Empirical modeling and process optimization by response surface methodology (RSM) and artificial neural network (ANN) simulation

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

An artificial neural network (ANN) simulation was utilized to determine the lapping parameters such as rotation speed, lapping duration and lapping pressure under a constant slurry supply for n-type crystalline Silicon (c-Si) wafers. Experiments were done with a Logitech PM5 lapping and polishing machine to obtain input data and target data for training, testing and validation of ANN. Lapping operation had five main parameters affecting surface quality: rotation speed, lapping duration, lapping pressure, flowrate of abrasive slurry and particle size in abrasive slurry. However, in this study slurry flowrate was assumed constant due the researches performed before. 218 lapping operations were performed with different values of the selected parameters and new lapping parameters were derived for different lapping conditions to achieve the best surface quality by using an ANN. In this study, wafers in 400 µm thickness cut under identical conditions from n-type single c-Si ingot in a STX 1202 DWS cutting machine were employed. Surface roughness (R a ) values were measured three times from different points of the wafers after lapping with a contact type surface roughness measurement tool using a microscopic scale stylus profiler (SP). In ANN simulation 70% of R a values were utilized for training, 15% of R a values were utilized for validation and 15% of R a values were utilized for test data. Results obtained from ANN simulation validated with a success above 99%.

This study presents establishment of multiple input prediction model for automotive coil spring fatigue life estimation to shorten automotive suspension design process. Automotive suspension design is a lengthy work where any changes of the design lead to repetition of the entire process. It was hypothesised that the established model could be used to predict the spring design fatigue life without using any strain measurements. To initiate this model establishment, five sets of strain and acceleration measurement across different road conditions were collected and used for validations. To include spring stiffness as a parameter, a quarter car model was generated to obtain the force time histories from spring and vertical vibration of vehicle mass. In addition, artificial road profiles of road classes “A” to “D” were also generated for the quarter car simulation. Through adjusting the spring stiffness in the quarter car model, the spring and vehicle responses were varied. The simulated force time histories were used to predict springs’ fatigue life while acceleration time histories were used to calculate ISO 2631 ride-related vertical vibration. Subsequently, multiple linear regression approach was applied to determine the relationship between vehicle body frequency, ISO 2631 ride-related vertical vibration and spring fatigue life. The obtained regression had shown significance to the spring fatigue life with coefficient of determination of 0.8320. Reciprocally, multiple linear regression models were also used to predict the ISO 2631 ride-related vertical vibration with a coefficient of determination at 0.8810 and mean squared error values below 0.3430. To optimise the prediction results, artificial neural network was trained for the fatigue and vibration prediction purposes. The architectures of the artificial neural network were designed in terms of number of neurons and hidden layers to achieve a higher coefficient of determination of 0.9926 and lower mean squared error of 0.0824. For vibration prediction, the vehicle body frequency and spring fatigue life has shown a significant coefficient of determination to the ISO 2631 weighted vertical vibration, reaching 0.9579 with mean squared error of 0.0004. Based on the experimental strain and acceleration results, the predicted fatigue lives of multiple linear regression models were correlated well with the experimental results with coefficient of determination value of 0.9275. Meanwhile, the maximum difference of vibration prediction to experimental value using multiple linear regression models was only 18%. For artificial neural network predictions, the fatigue lives were mostly distributed within 1:2 or 2:1 life correlation and vibration prediction results were within 12%. For a good prediction, the target correlation value was above 0.80 to demonstrate a good fitted curve and the difference below 20%. The trained artificial neural network has shown outstanding capability in fatigue life or ride-related vertical vibration predictions. In this research, the main novelty was the trained artificial neural network for spring fatigue life or ride-related vertical vibration predictions which serve to reduce some procedures of automotive suspension design. The outcome of this study can be used to provide a new knowledge towards the field of fatigue research as well as vehicle ride dynamics. This research contributes to automotive industries especially in suspension spring design where the analysis of fatigue and ride-related vibration are provided.

One of the significant tasks in undercut slopes is determining the maximum stable undercut span. According to the arching effect theory, undercut excavations cause the weight of the slope to be transmitted to the adjacent stable regions of the slope, which will increase the stability of the slope. In this research, determining the maximum width of undercut slopes was examined through numerical modeling in the FLAC3D software. For this purpose, a series of undercut slope numerical models, with various slope angles, horizontal acceleration coefficients, and counterweight balance widths was conducted, and the results were validated using the corresponding experimental test results. The effect of each parameter on the maximum stable undercut span was investigated with an artificial neural network, where a multi-layer perceptron (MLP) model was performed. The results showed good accuracy of the proposed MLP model in the prediction of the maximum stable undercut span. In addition, a sensitivity analysis demonstrated that the dip angle and horizontal acceleration coefficient were the most and least effective input variables on the maximum stable undercut span, respectively.

A survey of approaches for implementing optical neural networks

EEG (Electroencephalograph) recording from the scalp has biological artifacts and external artifacts. Biological artifacts, which are generated, can be EMG (Electromyograph) signal, EOG (Electrooculograph) signal or ECG (Electrocardiograph) signal. These artifacts appear as noise in the recorded EEG signal individually or in a combined manner. Usually physicians are misled by these noisy signals and the EEG analysis can go wrong. This paper presents noise cancellation i.e. removal of noise signal which can be either EMG, ECG or a combination of these two artifacts from the corrupted EEG signal and also signal enhancement both using recurrent learning technique. For this purpose, we have implemented the RTRL (Real Time Recurrent Learning) algorithm, which is the most recent and sophisticated real time neural networks algorithm. This algorithm is coded using C language and is executed on the DSP processor TMS320CV5402. The obtained result is verified using MATLAB. Key words: Electroencephalograph (EEG), Recurrent Multilayer Perceptron (RMLP), Real Time Recurrent Learning (RTRL).

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

Side weir is one of the structures which are widely used in water engineering projects So study on the flow characteristics especially discharge coefficient ( ) of this type of weir is important. Several Empirical formulas proposed to calculate the that they usually associated with significant errors. Thus, using mathematical methods based on artificial intelligence is inevitable. Artificial neural network (ANN) is a very useful data-modeling tool that is able to capture and represent complex input and outputs relationships. In this study, accuracy the some famous empirical formula was assessed and Borghei and Parvaneh was most accuracy formula with ( ). To increase the accuracy of production the multi-layer neural network (MLP) has developed. This model, in the first, trained by a back propagation method such as levenberg_marquardt and its performance was determined. the error indexes in the train and testing process are equal to ( and ). To increase the accuracy of MLP model, instead of increasing the size of the network (increase in the number of neurons and layers), Genetic Algorithm was used to train this model (GANN)and obtained an optimized value for Bias and weights. The final results show that using the GA to training the MLP model cased to increase in accuracy .The performance of the optimized neural network (GANN) in training and testing is equal to ( and ). Normal 0 false false false EN-US X-NONE AR-SA

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.