A modified hidden weight optimization algorithm for feedforward neural networks

In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional networks. Because the feedforward wavelet neural network algorithm predicts the short-term traffic flow in different areas, it is necessary to examine the ability to predict the difference between different models. From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. Feedforward neural network algorithm: we develop and obtain the best short-term high-speed traffic forecast values. The results are as follows: after using the author’s algorithm, the processing time increases by 1.5 seconds, but the average percentage of errors decreases by more than 50%, in fact the error and the root mean square error decreased by about 30%, and the smoothing coefficient increased by about 1%. The prediction of the author’s algorithm for short-term high-speed traffic is better than the wavelet neural network prediction algorithm, and the prediction accuracy and stability of the author’s algorithm are higher.

A fast and robust algorithm for training feedforward neural networks (FNNs) by using a variable forgetting factor and U-D factorization-based recursive prediction error (RPE) method is proposed. In comparison with the backpropagation (BP) and RPE based learning algorithms, the proposed algorithm, called UD-RPE, can provide much more accurate learning results in fewer iterations with fewer hidden nodes and improve convergence rate and numerical stability (robustness). In addition, it is less sensitive to start-up parameters, such as initial weights and initial covariance matrix, and the randomness in the observed data. It also has good generalization ability and needs less learning time. Simulation results of nonlinear dynamic system modeling and identification show that the algorithm proposed here is an effective and efficient learning algorithm for FNNs.

Prediction of power losses in transformer cores using feed forward neural network and genetic algorithm

A fast and robust learning algorithm for feedforward neural networks

The unsteady recovery of oil and gas prices in early 2017 led to an increase in drilling and hydraulic fracturing operations in North America liquid-rich shale plays. The increasing number of producing wells, in addition to re-fractured wells, impose the need for optimizing field development strategies and shale gas supply chain networks that maximize profitability. Moreover, operators must account for undulating natural gas demands both locally and externally in a persistently low oil price environment. In this paper, we adopt supervised machine learning approaches to forecast local natural gas demand as well as guide well re-frac candidature. Both serve as critical inputs for maximizing the net present value of any shale gas field development project. An optimized water management structure is also incorporated in the proposed framework to account for associated produced-water recycling. Considering a shale gas network in the Marcellus play, supply chain optimization was achieved using a mixed integer non-linear programming formulation. The Strategic Planning model relies on at least 4 major efforts including reservoir simulation, which in turn relies on output from a feed forward Neural Network (NN) algorithm. The trained NN algorithm was deemed suitable for recommending re-frac candidates, necessary decision variables for multiphase reservoir simulation. Finally, NPV optimization relied on a four-layer Long Short-Term Memory (LSTM) recurrent neural network, developed for forecasting local shale gas demand. Both neural network algorithms were scripted using python. 17 non-redundant parameters were mined for 250 wells in the case study. The t-Distributed Stochastic Neighbor Embedding technique was used to visualize related low dimensional manifolds within the high-dimensional data set, and a NN algorithm was used to obtain probabilities of misclassification. The well-pads in the case study superstructure are modeled using compositional reservoir simulation. Each reservoir model is tuned by history matching production and pressure data for designated producing wells. Alternative field development strategies (including re-fracturing) are then simulated for a 10-year planning horizon to generate gas and water rate decline profiles. Using the LSTM developed, local gas demand is forecasted using data sets created from multivariate time-dependent local and global variables affecting shale gas demand. The LSTM algorithm is derived by convolving some features from the raw data set, with fined-tuned weights, with the objective of minimizing a pre-defined demand error function. The results obtained show that application of this integrated approach can give operators a calculated advantage, preventing erroneous feedback of project profitability but allowing early time decision-making that maximizes shale asset NPV.

Disease is crucial to prevent more serious complications. This study implemented a Feedforward Neural Network (FNN) algorithm to build a cardiovascular disease risk prediction model using patient clinical data. The dataset used was sourced from open sources and underwent preprocessing stages such as one-hot encoding and normalization. The model architecture consists of two hidden layers with ReLU and dropout activation functions, and an output layer with a sigmoid function for binary classification. Training was conducted for 100 epochs with a data split ratio of 80:20. Evaluation was carried out using accuracy, precision, recall, F1-score, and confusion matrix metrics. The evaluation results showed that the model achieved a training accuracy of 92% and a testing accuracy of 88%, with an average F1-score of 87.2%. The Confidence Factor value also indicated a high level of confidence in each prediction. These results indicate that the FNN model is effective for cardiovascular disease risk prediction and has the potential to be used as a tool for rapid and accurate medical decision-making.

Image classification has gained an important role in many applications (landscape planning or assessment, meteorology, biodiversity, etc...). This operation aims to separate different regions of an image having various properties in different classes. In this paper, a back-propagation clustering network is proposed for efficient image classification. Our goal is to be able to determine with accuracy some (ROI) regions of interest in a gray level image. To this end, we introduce a new approach that optimizes network performance and improve its learning using genetic algorithms (GA). GAs are applied to optimize internal parameters of the network structure (weights and bias) through a fitness function. The proposed approach generates classification results with high accuracy and reliability. A comparison study is conducted and proved that the combination between a feed forward neural network and genetic algorithm generates better results than other recent methods in the literature based only on neural network trained with backpropagation algorithm.

An implementation of the Levenberg–Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed-forward neural networks

In this paper a general class of fast learning algorithms for feedforward neural networks is introduced and described. The approach exploits the separability of each layer into linear and nonlinear blocks and consists of two steps. The first step is the descent of the error functional in the space of the outputs of the linear blocks (descent in the neuron space), which can be performed using any preferred optimization strategy. In the second step, each linear block is optimized separately by using a least squares (LS) criterion. To demonstrate the effectiveness of the new approach, a detailed treatment of a gradient descent in the neuron space is conducted. The main properties of this approach are the higher speed of convergence with respect to methods that employ an ordinary gradient descent in the weight space backpropagation (BP), better numerical conditioning, and lower computational cost compared to techniques based on the Hessian matrix. The numerical stability is assured by the use of robust LS linear system solvers, operating directly on the input data of each layer. Experimental results obtained in three problems are described, which confirm the effectiveness of the new method.

The convergence of a class of stochastic pseudo-gradient algorithms driven by correlated data sequences is considered in this paper. The obtained results are applied to a learning algorithm for feedforward neural networks and sufficient conditions for its convergence are determined.

Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment

Forecasting the monsoon temporally is a major scientific issue in the field of monsoon meteorology. The ensemble of statistics and mathematics has increased the accuracy of forecasting of Indian summer monsoon rainfall (ISMR) up to some extent. But due to the nonlinear nature of ISMR, its forecasting accuracy is still below the satisfactory level. Mathematical and statistical models require complex computing power. Therefore, many researchers have paid attention to apply artificial neural network in ISMR forecasting. In this study, we have used feed-forward back-propagation neural network algorithm for ISMR forecasting. Based on this algorithm, we have proposed the five neural network architectures designated as BP1, BP2, $$\ldots, $$ BP5 using three layers of neurons (one input layer, one hidden layer and one output layer). Detail architecture of the neural networks is provided in this article. Time series data set of ISMR is obtained from Pathasarathy et al. (Theor Appl Climatol 49:217–224 1994) (1871–1994) and IITM ( http://www.tropmet.res.in/ , 2012) (1995–2010) for the period 1871–2010, for the months of June, July, August and September individually, and for the monsoon season (sum of June, July, August and September). The data set is trained and tested separately for each of the neural network architecture, viz., BP1–BP5. The forecasted results obtained for the training and testing data are then compared with existing model. Results clearly exhibit superiority of our model over the considered existing model. The seasonal rainfall values over India for next 5 years have also been predicted.

A new fast learning algorithm for a multilayer feedforward neural network is presented. The new algorithm, based on an innovative variable step size and gradient averaging, has excellent properties to overcome major drawbacks of the backpropagation algorithm and shows high convergence speed. The improved performance of the new algorithm in comparison with other algorithms is verified via computer simulations.

Linear programming algorithm for neural networks

A modified ELM algorithm for a class of single-hidden layer feedforward neural networks (SLFNs) with linear nodes is discussed in this paper. It is seen that the input weights of the SLFN are designed such that the hidden layer performs as a preprocessor for removing the effects of the input disturbance and reducing both the structural and the empirical risks, the output weights are then trained to minimize the output error and further balance and reduce the structural and the empirical risks of the SLFN. The performance of an SLFN-based classifier trained with the proposed scheme is evaluated in the simulation section in support of the proposed scheme.