Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Multiple criteria assessment of methods for forecasting building thermal energy demand
2020
Abstract Nowadays worldwide directives have focused the attention on improving energy efficiency in the building sector. The research of models able to predict the energy consumption from the first design and energy planning phase is conducted to improve building sustainability. Use of traditional forecasting tools for building thermal energy demand tends to encounter difficulties relevant to the amount of data required, implementation of the models, computational costs and inability to generalize the output. Therefore, many studies focused on the research and development of alternative resolution methods, but the choice of the most convenient is not clear and simple. Single comparison of s…
Unbiased sensitivity analysis and pruning techniques in neural networks for surface ozone modelling
2005
Abstract This paper presents the use of artificial neural networks (ANNs) for surface ozone modelling. Due to the usual non-linear nature of problems in ecology, the use of ANNs has proven to be a common practice in this field. Nevertheless, few efforts have been made to acquire knowledge about the problems by analysing the useful, but often complex, input–output mapping performed by these models. In fact, researchers are not only interested in accurate methods but also in understandable models. In the present paper, we propose a methodology to extract the governing rules of trained ANN which, in turn, yields simplified models by using unbiased sensitivity and pruning techniques. Our propos…
Hybrid prediction-optimization approaches for maximizing parts density in SLM of Ti6Al4V titanium alloy
2022
AbstractIt is well known that the processing parameters of selective laser melting (SLM) highly influence mechanical and physical properties of the manufactured parts. Also, the energy density is insufficient to detect the process window for producing full dense components. In fact, parts produced with the same energy density but different combinations of parameters may present different properties even under the microstructural viewpoint. In this context, the need to assess the influence of the process parameters and to select the best parameters set able to optimize the final properties of SLM parts has been capturing the attention of both academics and practitioners. In this paper differ…
An Adaptive Global-Local Memetic Algorithm to Discover Resources in P2P Networks
2007
This paper proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing the training of the neural network. This training is very challenging due to the large number of weights and noise caused by the dynamic neural network testing. The AGLMA is a memetic algorithm consisting of an evolutionary framework which adaptively employs two local searchers having different exploration logic and pivot rules. Furthermore, the AGLMA makes an adaptive noise compensation by means of explicit averaging on the fitness values and a dynamic population sizing which aims to follow the ne…
Combining a context aware neural network with a denoising autoencoder for measuring string similarities
2020
Abstract Measuring similarities between strings is central for many established and fast-growing research areas, including information retrieval, biology, and natural-language processing. The traditional approach to string similarity measurements is to define a metric with respect to a word space that quantifies and sums up the differences between characters in two strings; surprisingly, these metrics have not evolved a great deal over the past few decades. Indeed, the majority of them are still based on making a simple comparison between character and character distributions without considering the words context. This paper proposes a string metric that encompasses similarities between str…
Optimal Pruned K-Nearest Neighbors: OP-KNN Application to Financial Modeling
2008
The paper proposes a methodology called OP-KNN, which builds a one hidden-layer feed forward neural network, using nearest neighbors neurons with extremely small computational time. The main strategy is to select the most relevant variables beforehand, then to build the model using KNN kernels. Multi-response sparse regression (MRSR) is used as the second step in order to rank each k-th nearest neighbor and finally as a third step leave-one-out estimation is used to select the number of neighbors and to estimate the generalization performances. This new methodology is tested on a toy example and is applied to financial modeling.
Problems of coding stereo images in human memory
2010
This paper discusses the memorization and recall by man of a sequence of planar or stereoscopic images, including six frames that contain a planar strip (8×8 positions of the stimulus) or a volume strip (8×4×2 positions). At the recall stage, the subject chose between the stimulus and three distractors in each frame. It is shown that the times for recognition and recall are less for volume stimuli, while the percent of correct responses is greater for planar stimuli. For volume stimuli, the distribution of errors depends on the disparity between the target and the selected distractor. A model based on a heteroassociative neural network reproduces the error distribution for planar but not fo…
Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping
2016
The choice of the proper resolution in landslide susceptibility mapping is a worth considering issue. If, on the one hand, a coarse spatial resolution may describe the terrain morphologic properties with low accuracy, on the other hand, at very fine resolutions, some of the DEM-derived morphometric factors may hold an excess of details. Moreover, the landslide inventory maps are represented throughout geospatial vector data structure, therefore a conversion procedure vector-to-raster is required.This work investigates the effects of raster resolution on the susceptibility mapping in conjunction with the use of different algorithms of vector-raster conversion. The Artificial Neural Network t…
A new method for optimal synthesis of wavelet-based neural networks suitable for identification purposes
1999
Abstract This paper deals with a new method for optimal synthesis of Wavelet-Based Neural Networks (WBNN) suitable for identification purposes. The method uses a genetic algorithm (GA) combined with a steepest descent technique and least square techniques for both optimal selection of the structure of the WBNN and its training. The method is applied for designing a predictor for a chaotic temporal series