Using fractional derivative in learning algorithm for artificial neural network: Application for salary prediction

This paper presents the development of several efficient learning algorithms for neural networks (ELEANNE). The ELEANNE 1 and ELEANNE 2 are two recursive least-squares learning algorithms, proposed for training single-layered neural networks with analog output. This paper also proposes a new optimization strategy for training single-layered neural networks, which provides the basis for the development of a variety of efficient learning algorithms. This optimization strategy is the source of the ELEANNE 3, a second-order learning algorithm for training single-layered neural networks with binary output. A simplified version of this algorithm, called ELEANNE 4, is also derived on the basis of some simplifying but reasonable assumptions. The two algorithms developed for single-layered neural networks provide the basis for the derivation of ELEANNE 5 and ELEANNE 6, which are proposed for training multilayered neural networks with binary output. The ELEANNE 7 is an efficient algorithm developed for training multilayered neural networks with either binary or analog output. >

A highly efficient implementation of a backpropagation learning algorithm using matrix ISA

Machine learning algorithms are used to analyze medical data sets effectively in present. Today machine learning gives us several necessary tools for intelligent data analysis and research. Especially in very recent years, the digital world has provided relatively inexpensive and available means to collect and store the data. The main aim is to implement supervised machine learning concept by using datasets regarding blood cells collected from blood cells detecting and counting sensors, of a human as the input which is trained by artificial neural network algorithm and apply decision tree classification learning algorithm to perform classification which results in recognizing and also possibly predict the disease based on the nature of blood cells and classify accordingly. Artificial Neural network algorithm seems to avoid pruning problem and has higher efficiency and accuracy in training the datasets. Also the using of Decision tree is because they are easy to interpret, understand and also possess non-linear characteristics between values. This holds well the performance of the tree constructed which gives better outputs. Beside all the application developed using machine learning in day today life, the use of such learning algorithm in such medical application will enhance and benefit the medical field.

This chapter presents basic definitions and characteristics of fractional calculus. Definitions and characteristics of the Riemann–Liouville fractional integral and derivative, Caputo fractional derivative, Grünwald–Letnikov fractional derivative, Riesz fractional derivative, modified Riemann–Liouville derivative, and local fractional derivative have been primarily explored here. However, the Riemann–Liouville definitions of fractional differentiation play a significant role in developing fractional calculus. Because the derivative of constant is not zero, the Riemann–Liouville definition is not widely applicable. In such scenarios, the Caputo fractional derivative is more suited than the standard Riemann–Liouville derivative for applications in various engineering problems. It may be noted that the Riemann–Liouville technique is challenging to understand physically, while the Caputo approach employs integer-order initial conditions, which are easier to apply in real-world applications. This chapter also covers the Euler psi function, incomplete gamma function, beta function, incomplete beta function, Wright function, Mellin-Ross function, error function, hypergeometric functions such as Gauss, Kummer, and extended hypergeometric functions, and the H -function.

Floods are natural disasters that often occur in the DKI Jakarta area. DKI Jakarta government needs to anticipate the impact of the flood disaster by estimating the number of people affected by the flood. The number of people affected by floods that are uncertain every month can be predicted for the future. There are many ways that can be predict the number of people affected by floods, one of them is artificial neural network method. One of learning algorithms in artificial neural networks is backpropagation algorithm. This research applies an artificial neural network method with backpropagation algorithm to predict the number of people affected by floods in DKI Jakarta. In this research, training process was carried out 100 times on each network architecture (12-10-1, 12-12-1, 12-14-1) with several parameters such as epoch, momentum constant, and learning rate. The best results in the training process are carried out to testing process to test the network. In the testing process, the best results are 12-10-1 architecture with an accuracy rate 98.704%. Based on these results, it can be said that this network can predict well and can be implemented for forecasting the number of people affected by floods in DKI Jakarta.

PurposeThe paper explores the spectral solution of a partial differential equation governing nonlinear transmission lines (NLTLs). It aims to present an efficient simulation technique for a nonlinear fractional-order transmission line model and to show the effect of fractional derivatives on the evolution of solitons and wave packets of increasing spatial frequency.Design/methodology/approachThe paper is concerned with the solution of the equation governing NLTLs for both integer and fractional time derivatives. The spectral method shall be considered with Hermite and Malmquist-Takenaka orthogonal functions and integer and fractional implementations of the implicit trapezoidal rule. While the Hermite functions and Malmquist-Takenaka functions are suitable for solitons, the Malmquist-Takenaka functions are selected as these are superior for approximating wave packets of increasing frequency (Iserles, Luong and Webb, 2023). Furthermore, they possess properties that are favourable from a computational viewpoint.FindingsResults will show that the proposed approach is effective for simulating NLTLs with both integer and fractional time derivatives. In particular, fractional derivatives are shown to have a significant effect on the evolution of wave packets on a nonlinear transmission line.Originality/valueThe method is novel in using Malmquist-Takenaka basis functions for a spectral solution of a nonlinear transmission line equation with both integer and fractional time derivatives and for examining the effect of fractional derivatives on wave packets.

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

The Cascade Correlation learning algorithm is a special supervised learning algorithm for artificial neural network architecture. The optimization algorithm in the traditional neural network has the disadvantages of a single optimization goal, slow convergence speed, and can easily fall into local area, which cannot fully meet the key elements in the cascade correlation learning algorithm. In comparison, the group intelligence optimization algorithm can take into account these key elements in the optimization process at the same time, and obtain better optimization results. In this paper, we propose the single-objective optimization algorithm jDE-B and the multi-objective optimization algorithm MOEA-T, and improve the network expansion mode in the learning process of Cascade Correlation neural networks. We investigate the effect of applying the group intelligent optimization algorithm in the Cascade Correlation learning algorithm. Experimental results show that our improved algorithm is able to enhance the ability of the Cascade Correlation neural network to fit problems, reduce the number of hidden units and the depth of the network, and optimize the network structure.

A way in which a neural network can implement a genetic algorithm as its learning algorithm is shown. This model is called GLANN (genetic learning algorithm for neural networks). The components of GLANN can be shown to be biologically plausible. The algorithm itself can be classified as a reinforcement learning algorithm. The neural network has a fixed architecture and processes binary strings using genetic operators. Learning is stored in the form of newly created patterns, which can then be stored in some kind of associative memory. The benefits of GLANN reside in the proven optimizing capabilities of genetic algorithms, and in its parallel implementation. The shallow two-level architecture translates into system scalability, an issue that has not been successfully resolved in the case of other neural network algorithms. >

In this work, we propose a quantum algorithm to evaluate neural networks architectures named Quantum Neural Network Architecture Evaluation (QNNAE). The proposed algorithm is based on a quantum associative memory and the learning algorithm for artificial neural networks. Unlike conventional algorithms for evaluating neural network architectures, QNNAE does not depend on initialization of weights. The proposed algorithm has a binary output and results in 0 with probability proportional to the performance of the network. And its computational cost is equal to the computational cost to train a neural network.

With the advantages of simple structure, high electromagnetic (EM) shielding effectiveness (SE), wide shielding bandwidth and adjustable transmittance, the metal grid is a popular tool in the field of electromagnetic shielding. In this work, the deep learning algorithm of artificial neural network was used to predict the EM SE of the metal grid. After certain number of trainings, both the SE spectrum and the minimum SE value in a given frequency band can be predicted accurately and effectively. On the other hand, for a target SE, the design parameters of the metal grid structure can be immediately predicted. The results show that the relative mean square deviation of the predicted SE curve is about 5%, the relative error of the minimum SE value of the curve is less than 5%, and the average relative error of the minimum SE value in the predicted frequency band is 11.5%.

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.

The Sixth International Symposium on Neural Networks (ISNN 2009)

An analytical learning algorithm to find the weight matrix of the dendro-dendritic neural network is presented. This learning algorithm utilizes linear programming to find all (necessarily) non-negative and small weights. A sufficient stability criterion is given, which guarantees all stored patterns to be asymptotically stable. >

We estimate the spectrum envelope of speech by using neural networks. The neural networks system proposed in our study consists of two parts. The first part is the network for detecting the spectrum peaks, and the other is for interpolating between the extracted peaks. The structure of the latter is derived from the Neville interpolation. The former is based on the peak picking method. We present an operator for detecting the peaks using neural networks. Unfortunately, the result is that the neural networks do not only detect the general peaks but also the local peaks and the extremely closed peaks. We train the neural networks to detect only the general peaks. The learning algorithm uses a steepest descent method such as the backpropagation (BP) algorithm. After the learning, we show the detected peaks and the spectrum envelope when we use the learned pattern as the input data. In addition, we perform an experiment to examine whether the proposed operator corresponds to the XOR Boolean function or not.