Extended memory loading capacity of a neutral network model with delayed time interaction

1992 ◽  
Vol 162 (4) ◽  
pp. 327-330 ◽  
Author(s):  
P. Sen ◽  
B.K. Chakrabarti
Keyword(s):  
Network Model ◽  
Neutral Network ◽  
Loading Capacity ◽  
Time Interaction ◽  
Delayed Time

scholarly journals Emergent speciation by multiple Dobzhansky-Muller incompatibilities

2014 ◽  
Author(s):  
Tiago Paixão ◽  
Kevin E. Bassler ◽  
Ricardo B. R. Azevedo
Keyword(s):  
Gene Flow ◽  
Reproductive Isolation ◽  
Network Model ◽  
Neutral Network ◽  
Collective Effects ◽  
Neutral Networks ◽  
Adaptive Landscapes ◽  
Complex Interactions ◽  
Or Gene

The Dobzhansky-Muller model posits that incompatibilities between alleles at different loci cause speciation. However, it is known that if the alleles involved in a Dobzhansky-Muller incompatibility (DMI) between two loci are neutral, the resulting reproductive isolation cannot be maintained in the presence of either mutation or gene flow. Here we show that speciation can emerge through the collective effects of multiple neutral DMIs that cannot, individually, cause speciation-a mechanism we call emergent speciation. We investigate emergent speciation using models of haploid holey adaptive landscapes-neutral networks-with recombination. We find that certain combinations of multiple neutral DMIs can lead to speciation. Furthermore, emergent speciation is a robust mechanism that can occur in the presence of migration, and of deviations from the assumptions of the neutral network model. Strong recombination and complex interactions between the DMI loci facilitate emergent speciation. These conditions are likely to occur in nature. We conclude that the interaction between DMIs may cause speciation.

scholarly journals Multigrades Classification Model of Magnesite Ore Based on SAE and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yachun Mao ◽  
Dong Xiao ◽  
Jinfu Cheng ◽  
Defu Che ◽  
Batuan Le ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Network Model ◽  
Great Influence ◽  
Raw Material ◽  
Neutral Network ◽  
Classification Model ◽  
Smelting Process ◽  
Stacked Autoencoder ◽  
Grade Classification ◽  
Learning Machine

Magnesite is an important raw material for extracting magnesium metal and magnesium compound; how precise its grade classification exerts great influence on the smelting process. Thus, it is increasingly important to determine fast and accurately the grade of magnesite. In this paper, a method based on stacked autoencoder (SAE) and extreme learning machine (ELM) was established for the classification model of magnesite. Stacked autoencoder (SAE) was firstly used to reduce the dimension of magnesite spectrum data and then neutral network model of extreme learning machine (ELM) was adopted to classify the data. Two improved extreme learning machine (ELM) models were employed for better classification, namely, accuracy extreme learning machine (AELM) and integrated accuracy (IELM) to build up the classification models. The grade classification through traditional methods such as chemical approaches, artificial methods, and BP neutral network model was compared to that in this paper. Results showed that the classification model of magnesite ore through stacked autoencoder (SAE) and extreme learning machine (ELM) is better in terms of speed and accuracy; thus, this paper provides a new way for the grade classification of magnesite ore.

A Novel Grid Phase Estimating Algorithm Based on BP Neutral Network

2012 ◽  
Vol 239-240 ◽  
pp. 1507-1510
Author(s):  
Li Na Zhang ◽  
Jin Ming Chang
Keyword(s):  
Network Model ◽  
Neutral Network ◽  
Phase Lock ◽  
Voltage Distortion ◽  
Digital Phase ◽  
Wide Range ◽  
Voltage Signal ◽  
Network Program ◽  
Unbalanced Loads

With respect to the voltage distortion caused by harmonics and unbalanced loads, traditional digital phase lock algorithm doesn't work properly. And this paper presents a novel estimating algorithm based on BP neutral network model. The voltage signal is the input of the network and then by training the network with the prepared samples, the network can calculate the grid phase correctly. This algorithm has no relationship with the harmonics so it can work in a wide range. A network program is finished and the algorithm is validated. The results show that the algorithm works effectively and the result is correct.

A Network Model for Optimizing two Echelon Multi-Item Repair Level Decisions

1991 ◽  
Vol 8 (1) ◽  
pp. 77-90
Author(s):  
W. Steven Demmy ◽  
Lawrence Briskin
Keyword(s):  
Network Model

Validating Four Standard Scales in Spiritually Practicing and Nonpracticing Samples Using Propensity Score Matching

2008 ◽  
Vol 24 (3) ◽  
pp. 165-173 ◽  
Author(s):  
Niko Kohls ◽  
Harald Walach
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Repeated Measures ◽  
Propensity Scores ◽  
Total Sample ◽  
Experimental Manipulation ◽  
Brief Symptom Inventory ◽  
Time Interaction ◽  
Social Support Scale ◽  
Lower Test

Validation studies of standard scales in the particular sample that one is studying are essential for accurate conclusions. We investigated the differences in answering patterns of the Brief-Symptom-Inventory (BSI), Transpersonal Trust Scale (TPV), Sense of Coherence Questionnaire (SOC), and a Social Support Scale (F-SoZu) for a matched sample of spiritually practicing (SP) and nonpracticing (NSP) individuals at two measurement points (t1, t2). Applying a sample matching procedure based on propensity scores, we selected two sociodemographically balanced subsamples of N = 120 out of a total sample of N = 431. Employing repeated measures ANOVAs, we found an intersample difference in means only for TPV and an intrasample difference for F-SoZu. Additionally, a group × time interaction effect was found for TPV. While Cronbach’s α was acceptable and comparable for both samples, a significantly lower test-rest-reliability for the BSI was found in the SP sample (rSP = .62; rNSP = .78). Thus, when researching the effects of spiritual practice, one should not only look at differences in means but also consider time stability. We recommend propensity score matching as an alternative for randomization in variables that defy experimental manipulation such as spirituality.

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