A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons

Chaudhary, Vikas; Bhatia, R.; Ahlawat, Anil

doi:10.1016/j.aej.2014.09.007

Cited by 46 publications

(19 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The SOM algorithm classifies data into the nearest neurons by calculating the distance between the data input of the input layer and the neurons of the output layer [28]. First, the Euclidean distance between the input data and the weight of output neuron is achieved to determine the nearest neuron as the winner neuron W, calculated as follows [29]:…”

Section: Clustering Methodologymentioning

confidence: 99%

“…where ω ij (t) is the output neuron vector at the number of iterations t, and x(t) is the input data. The determined winner neuron W is updated by the following steps [29]:…”

Section: Clustering Methodologymentioning

confidence: 99%

“…The arbitrage utilizes differences of energy usage charge rates. This algorithm saves the customers' energy usage charge by charging their ESS during the hours of the relatively lower usage charge rate and then discharging the energy during the hours of the relatively higher usage charge rate [29]. The daily charge-discharge cycles for the arbitrage operation can be selected to a single cycle (1-cycle) or multiple cycles depending on the TOU schedules.…”

Section: Operational Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

2020

View full text Add to dashboard Cite

This study proposes a methodology to develop adaptive operational strategies of customer-installed Energy Storage Systems (ESS) based on the classification of customer load profiles. In addition, this study proposes a methodology to characterize and classify customer load profiles based on newly proposed Time-of-Use (TOU) indices. The TOU indices effectively distribute daily customer load profiles on multi-dimensional domains, indicating customer energy consumption patterns under the TOU tariff. The K-means and Self-Organizing Map (SOM) sophisticated clustering methods were applied for classification. Furthermore, this study demonstrates peak shaving and arbitrage operations of ESS with current supporting polices in South Korea. Actual load profiles accumulated from customers under the TOU rate were used to validate the proposed methodologies. The simulation results show that the TOU index-based clustering effectively classifies load patterns into 'M-shaped' and 'square wave-shaped' load patterns. In addition, the feasibility analysis results suggest different ESS operational strategies for different load patterns: the 'M-shaped' pattern fixes a 2-cycle operation per day due to battery life, while the 'square wave-shaped' pattern maximizes its operational cycle (a 3-cycle operation during the winter) for the highest profits. Energies 2020, 13, 1723 2 of 17 algorithms [6]. Lee et al. classified the electric power consumption characteristics of industrial customers using the standard industry classification code in South Korea [7]. Bidoki et al. applied different clustering algorithms based on K-means, weighted fuzzy mean K-means, modified follow leader (MFTL), self-organizing map (SOM), and layer algorithm to classify load curves of different types of customers, and the results from comparing the clustering performances utilized to determine the adaptive clustering algorithms [8]. Zhou et al. proposed the five-stage process model based on K-means, SOM, Fuzzy c-average (FCM), and hierarchical clustering algorithms to analyze the impact of electric power suppliers and their consumers in smart grid circumstances [9]. Abubaker classified load profiles achieved from electricity consumers in the Tulkarm district based on the K-means algorithm [10].These clustering-based approaches for analyzing customer load profiles can be applied to the development of effective strategies for energy management in smart grid circumstances. The representative strategy utilizes the Energy Storage System (ESS) installed for electricity consumers [11]. The ESS is a device that enables the storage of electrical energy during off-peak times and supplies the stored energy at the requested time to reduce electricity costs for the customer. The South Korean government had established a long-term roadmap and supporting policies to increase the penetration level of ESS, and, consequently, South Korea has been positioned as the second largest nameplate capacity of ESS since 2016 [12].Several studies have proposed methodologies to optimize and deve...

show abstract

Section: Clustering Methodologymentioning

confidence: 99%

“…where ω ij (t) is the output neuron vector at the number of iterations t, and x(t) is the input data. The determined winner neuron W is updated by the following steps [29]:…”

Section: Clustering Methodologymentioning

confidence: 99%

Section: Operational Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

2020

View full text Add to dashboard Cite

show abstract

“…For example, only winner neurons weights are updated in competitive neural processing (Abdipoor et al 2013). Likewise, winner neurons and specified topological neighbour neurons weights are updated in Kohonen learning or maps (Chaudhary et al 2014;Hasan and Shamsuddin 2011). Hence, it is possible to deduce which neurons respond optimally to which patterns or stimuli.…”

Section: Investigated Hypothesesmentioning

confidence: 99%

Banknote recognition: investigating processing and cognition framework using competitive neural network

Oyedotun

Khashman

2016

Cogn Neurodyn

View full text Add to dashboard Cite

Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria's Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.

show abstract

“…In one of these studies, Chaudhary et al (2014) modified the classical SOM in a way that as well as the farthest and nearest neurons from the winner neuron, the winning frequency of each neuron was taken into account for updating the weight [3]. In another study, Ghaseminezhad and Karami (2011) presented a novel SOM-based algorithm for clustering discrete groups of data and they indicated the classic SOM algorithm could not cluster discrete data correctly [4].…”

Section: Introductionmentioning

confidence: 99%

Self-organizing maps with sliding window (SOM+SW)

et al. 2017

View full text Add to dashboard Cite

Original scientific paper SOM is a popular artificial neural network algorithm to perform rational clustering on many different data sets. There is a disadvantage of the SOM that can run on a predefined completed data set. Various problems are encountered on a time-stream data sets when clustering by using standard SOM since the time-stream data sets are generated dependent on time. In this study, the Sliding Window feature is included into standard SOM for clustering timestream data sets. Thus, the combination of SOM and Sliding Window (SOM + SW) gives more accurate results when clustering on time-stream data sets. To prove this, a set of internet usage data from a mobile operator in Turkey is taken to test. The taken data set from the mobile operator is clustered according to the classical SOM then the future data usages of subscribers are estimated. The same data set is applied on the SOM + SW to perform the same simulations. Keywords: clustering; mobile operators; self-organizing maps (SOM); sliding window; time-stream data sets Samoorganizirane mape s kliznim prozorom (SOM + SW)Izvorni znastveni članak SOM je popularan algoritam umjetne neuronske mreže za obavljanje racionalnog grupiranja na mnogim različitim skupovima podataka. Postoji nedostatak SOM-e koja se može izvoditi na unaprijed definiranom dovršenom skupu podataka. Na vremenskim tokovima skupova podataka pojavljuju se razni problemi prilikom grupiranja pomoću standardne SOM-e jer se vremenski tokovi podataka generiraju ovisno o vremenu. U ovoj studiji značajka kliznog prozora uključena je u standardnu SOM-u za grupiranje vremenskih tokova podataka. Stoga, kombinacija SOM i kliznog prozora (SOM + SW) daje točnije rezultate prilikom grupiranja podataka na vremenskom toku skupova podataka. Da bi se to dokazalo, testiran je skup podataka o uporabi interneta mobilnog operatora u Turskoj. Uzeti skup podataka mobilnog operatera grupiran je prema klasičnoj SOM-i, a zatim je procijenjena buduća uporaba podataka pretplatnika. Isti skup podataka primijenjen je na SOM + SW za izvođenje istih simulacija. Ključne riječi: grupiranje; klizni prozor; mobilni operateri; samoorganizirane mape (SOM); vremenski tok skupova podataka

show abstract

A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons

Cited by 46 publications

References 20 publications

Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

Banknote recognition: investigating processing and cognition framework using competitive neural network

Self-organizing maps with sliding window (SOM+SW)

Contact Info

Product

Resources

About