Modified Cascade Correlation Neural Network and its Applications to Multidisciplinary Analysis Design and Optimization in Ship Design

Schmitz, Adeline; Courouble, Frederick; Hefazi, Hamid; Besnard, Eric

doi:10.5772/9148

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Due to its manoeuvrability and ease of deployment, DCCM has found wide application in multidisciplinary contexts, ranging from weather prediction to civil engineering applications. To adapt it to new applications, several new methods have been recently developed, notably Evolving Cascade Neural Networks (ECNN, 2003) [ 18 ] and Modified Cascade Correlation Neural Networks (MCCNN, 2010) [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting floods with Flickr tags

2017

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Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags ‘#Sandy’ or ‘#flooding’), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak.

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Section: Methodsmentioning

confidence: 99%

Predicting floods with Flickr tags

2017

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“…Aday havuz sistemini kullanımı eğitimi kötü yönde etkileyecek ve onu durdurabilecek bir katmanın yüklenmesi riskini ortadan kaldırıyor. Fahlman ve Lebiere (1990), her bir havuzun içerisinde dört veya sekiz aday katmanın olma durumunun yeterli olacağını belirtmiştir [13].…”

Section: Basamak Korelasyon Sinir Ağı (Bksa)unclassified

Sınıflandırma Problemlerinin Karşılaştırılmasında ANFIS ve Basamak Korelasyon Sinir Ağının Kullanımı

Şengöz¹,

Özdemir²

2016

SDÜ Fen Bil Enst Der

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Anahtar Kelimeler Comparison of ANFIS and Cascade Correlation Neural Network Methods for Classification Problems KeywordsArtifical intelligence, Neural network, ANFIS, Cascade correlation neural network, CCNN Abstract: In this study, it focused on classification problems. This thesis different structure with 3 data set (Seed, Terrain Satellite Imaging and Red Wine Quality) examination and the same algorithms applied to these data as the relationship as well as the performance achievements and time in terms between the eyes reveals. Seed data is for the comparison with the ANFIS and CCNN test performance of 86.41% and 88.06% percent, in the classification process has always CCNN less time when viewed from the side. In the dataset Terrain Satellite Imaging performance ANFIS percentage of 100%, in the CCNN is 99.92%. The duration of the transactions made in the algorithm's data ANFIS 800 seconds, it completed the classification process CCNN all in 72 seconds. Our last data set is red wine quality evaluation and performance when viewed as ANFIS% 99 975, the CCNN is clearly not constitute a very significant difference between each other like 99 862%. According to the terms of processing time with the BKS method ANFIS 85, 271 seconds is far too cumbersome.

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“…This algorithm begins a minimal network, i.e without any hidden node, then automatically trains and adds new hidden unit one-by-one in a cascading maner. Scilicet, if the variance between the realized output and the targeted one is not low, it adds one hidden node [7], [8]. This candidate node is connected to all input nodes and previous added hidden units, i.e to all other nodes except the output nodes.…”

Section: Modified Cascade Correlation Algorithmmentioning

confidence: 99%

A Modified Semi-parametric Regression Model For Flood Forecasting

2015

Sci. Tech. Dev. J.

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In recent years, inundation, one of natural calamities, occurs frequently and fiercely. We are sustained severe losses in the floods every year. Therefore, the development of control methods to determine, analyze, model and predict the floods is indispensable and urgent. In this paper, we propose a justified semiparametric regression model for flood water levels forecasting. The new model has three components. The first one is parametric elements of the model. They are water level, precipitation, evaporation, air-humidity and groundmoisture values, etc. There is a complex connection among these parametrics. Several innovated regression models have been offered and experimented for this complicated relationship. The second one is a non-parametric ingredient of our model. We use the Arnak S. Dalalyan et al.’s effective dimension-reduction subspace algorithm and some modified algorithms in neural networks to deal with it. They are altered back-propagation method and ameliorated cascade correlation algorithm. Besides, we also propose a new idea to modify the conjugate gradient one. These actions will help us to smooth the model’s non-parametric constituent easily and quickly. The last component is the model’s error. The whole elements are essential inputs to operational flood management. This work is usually very complex owing to the uncertain and unpredictable nature of underlying phenomena. Flood-waterlevels forecasting, with a lead time of one and more days, was made using a selected sequence of past water-level values observed at a specific location. Time-series analytical method is also utilized to build the model. The results obtained indicate that, with a new semiparametric regression one and the effective dimension-reduction subspace algorithm, together with some improved algorithms in neural network, the estimation power of the modern statistical model is reliable and auspicious, especially for flood forecasting/modeling.

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Modified Cascade Correlation Neural Network and its Applications to Multidisciplinary Analysis Design and Optimization in Ship Design

Cited by 3 publications

References 21 publications

Predicting floods with Flickr tags

Predicting floods with Flickr tags

Sınıflandırma Problemlerinin Karşılaştırılmasında ANFIS ve Basamak Korelasyon Sinir Ağının Kullanımı

A Modified Semi-parametric Regression Model For Flood Forecasting

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