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title stringlengths 8 300	text stringlengths 0 10k	_id stringlengths 40 40	metadata dict
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design	An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority.	632589828c8b9fca2c3a59e97451fde8fa7d188d	{ "authors": [ "1725986" ], "cited_by": [ "93e1026dd5244e45f6f9ec9e35e9de327b48e4b0", "870cb11115c8679c7e34f4f2ed5f469badedee37", "7ee0b2517cbda449d73bacf83c9bb2c96e816da7", "97ca96b2a60b097bc8e331e526a62c6ce3bb001c", "f7d4fcd561eda6ce19df70e02b506e3201aa4aa7", "772f83c311649ad3ca2baf1c7c4de4610315a077", "0719495764d98886d2436c5f5a6f992104887160", "a1aa248db86001ea5b68fcf22fa4dc01016442f8", "a1877adad3b8ca7ca1d4d2344578235754b365b8", "8aedb834e973a3b69d9dae951cb47227f9296503", "1e5048d87fd4c34f121433e1183d3715217f4ab4", "b1c411363aded4f1098572f8d15941337310ca15", "05bd67f3c33d711f5e8e1f95b0b82bab45a34095", "f59f50a53d81f418359205c814f098be5fa7655a", "8cc9fa42cb88f0307da562bb7a8104cb2ed4474c", "c26229b43496b2fe0fa6a81da69928b378092d4d", "fe49526fef68e26217022fc56e043b278aee8446", 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A Hybrid EP and SQP for Dynamic Economic Dispatch with Nonsmooth Fuel Cost Function	Dynamic economic dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a new hybrid methodology for solving DED. The proposed method is developed in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a local search sequential quadratic programming (SQP) is used as a fine tuning to determine the optimal solution at the final. Ten units test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from EP and SQP alone.	86e87db2dab958f1bd5877dc7d5b8105d6e31e46	{ "authors": [ "30728239", "49115828", "1857220", "47952931" ], "cited_by": [ "8c6e8ac20aa8507879820a09ed4529d8e903e431", "6b79c84a38c1652c8a43d3726f1645066c67b6a8", "227aff5a5383424d01c4b5ab57555dcaca48f4ec", "977f815f19843a8a1769f5a4635183c2ebed6bc2", "fbd1ccdfcc3ba1881c1ad2ad84469c624cedf553", "b9b2a564a728f2535107e215af24f524d8ef2e47", "5055d0045c2322c0bf8ec306acbf33c39548d2c0", "9ed572bd9c0077f61e5b6f9d64318e7ffc37d52d", "b2273003c58d4639dd260fa41568bed2e64d0117", "4429253fccb908d46440c898884a818d5163a456", "35c78db509032d06b9efe04b32c3f5072d24eeda", "bbf532393866592ea0b18bd76441eceec415145b", "7f77cef8beadc0928dc869fed09f5191d52181a0", "a13d0a50bcde3400e73cd915dd540f2d4e376680", "6ff0f9903c2434d28d77b842710f7448b2235efe", "d897eed4426defe17b5ca9de76c448fe20c8d022", 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Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases	It's not surprisingly when entering this site to get the book. One of the popular books now is the genetic fuzzy systems evolutionary tuning and learning of fuzzy knowledge bases. You may be confused because you can't find the book in the book store around your city. Commonly, the popular book will be sold quickly. And when you have found the store to buy the book, it will be so hurt when you run out of it. This is why, searching for this popular book in this website will give you benefit. You will not run out of this book.	2a047d8c4c2a4825e0f0305294e7da14f8de6fd3	{ "authors": [ "1685850", "1699069", "34695695", "1841941" ], "cited_by": [ "ac1611bbe12f2dc91dad1d1ded3e618b0b848f21", "333f342ccfef65ad9e0d385dfdf4bb53bcfcc337", "cef8f8421dd57a75178d2149a77b57ef831ab256", "2e12a7ee656eba601d4f5921fda5c31b8e936a7e", "aef90296ccd328f56328b3f0cc991af97ef1b659", "1e8d666e4f0b7266419f51e623388fdd27613b83", "7c618007ef4094475da9ce97f4b7251c00a2cda6", "d451764a1d107ff5c968feae0fb2c1ad2c48b327", "76e24aac485b288322b2441362973c287fd939f3", "30fb791766bd615019da03039a863d1a4f35e454", "a78e23ff04ecb790407c1ce72d2fc6fe2397afb0", "f6a50e7159a947c5f84d7e5df6a3a60bf7badcba", "8d421814135c6625f53760b38c6c2d2e419506d2", "445c5dc1e6bbfaf871cc7fa42f79f1c8985f1d09", "0f592ba4de5e025bad3cf8bc99eb48fd5a498cb4", "7af97679056495cb13d84664adda37898c44b2ac", "904f5bc5230f66db888fbe5fa8b1999be88b6f9f", "1037388bdca86a6303d6dc133bb7181ff8238663", "1e3db6043c96d6965e63dc5099152f76575f3fc6", 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A modified particle swarm optimizer	"In this paper, we introduce a new parameter, called inertia weight, into the original particle swar(...TRUNCATED)	506172b0e0dd4269bdcfe96dda9ea9d8602bbfb6	{"authors":["8385459","4298485"],"cited_by":["019d49506e8fac0e964dbc52d1afc495c47df384","f51c70309ce(...TRUNCATED)
Identification and control of dynamic systems using recurrent fuzzy neural networks	"This paper proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlli(...TRUNCATED)	51317b6082322a96b4570818b7a5ec8b2e330f2f	{"authors":["34448377","2062864"],"cited_by":["4de9e6412d59169e624df02fc8c4e377a1f8be5d","ac7b545717(...TRUNCATED)
Separate face and body selectivity on the fusiform gyrus.	"Recent reports of a high response to bodies in the fusiform face area (FFA) challenge the idea that(...TRUNCATED)	857a8c6c46b0a85ed6019f5830294872f2f1dcf5	{"authors":["2981413","2074160","1931482"],"cited_by":["34bf37eb7a34ac4efc57254303f65429a3ccdd85","f(...TRUNCATED)
Scheduling for Reduced CPU Energy	"The energy usage of computer systems is becoming more important, especially for battery operated sy(...TRUNCATED)	12f107016fd3d062dff88a00d6b0f5f81f00522d	{"authors":["1800362","9036495","1686255","1753148"],"cited_by":["c3ce0da75953dd041152c1757d18647fe0(...TRUNCATED)
A data mining approach for location prediction in mobile environments	"Mobility prediction is one of the most essential issues that need to be explored for mobility manag(...TRUNCATED)	1ae0ac5e13134df7a0d670fc08c2b404f1e3803c	{"authors":["2108906","22789555","1801322","1796253"],"cited_by":["f1f25228e0285e615b84a150dec227978(...TRUNCATED)
$\mathsf {pSCAN}$ : Fast and Exact Structural Graph Clustering	"We study the problem of structural graph clustering, a fundamental problem in managing and analyzin(...TRUNCATED)	7d3c9c4064b588d5d8c7c0cb398118aac239c71b	{"authors":["38736958","35660624","36838704","19262604","47569211"],"cited_by":[],"references":["dd3(...TRUNCATED)
Synthesis, properties, and applications of iron nanoparticles.	"Iron, the most ubiquitous of the transition metals and the fourth most plentiful element in the Ear(...TRUNCATED)	305c45fb798afdad9e6d34505b4195fa37c2ee4f	{"authors":["5701357"],"cited_by":["82b17ab50e8d80c81f28c22e43631fa7ec6cbef2","649ad261855d2854f2509(...TRUNCATED)

End of preview. Expand in Data Studio

YAML Metadata Warning: The task_categories "information-retrieval" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

BEIR SCIDOCS Dataset (Migrated)

This is a migrated version of BeIR/scidocs that is compatible with datasets library 4.0.0+.

Dataset Description

This dataset contains the scidocs dataset from the BEIR benchmark, converted from the old script-based format to Parquet format.

Dataset Structure

Queries

Split 'queries': 1,000 examples
- Features: ['_id', 'text', 'metadata']
Total examples: 1,000

Corpus

Split 'corpus': 25,657 examples
- Features: ['_id', 'title', 'text', 'metadata']
Total examples: 25,657

Usage

from datasets import load_dataset

# Load queries (split: queries)
queries = load_dataset("Hyukkyu/beir-scidocs", "queries", split="queries")

# Load corpus (split: corpus)
corpus = load_dataset("Hyukkyu/beir-scidocs", "corpus", split="corpus")

Available Splits

Queries

queries: 1,000 examples

Corpus

corpus: 25,657 examples

Original Dataset

This dataset is migrated from: BeIR/scidocs

Citation

If you use this dataset, please cite the original BEIR paper:

@article{thakur2021beir,
  title={BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
  author={Thakur, Nandan and Reimers, Nils and Ruckle, Andreas and Srivastava, Abhishek and Gurevych, Iryna},
  journal={arXiv preprint arXiv:2104.08663},
  year={2021}
}

Downloads last month: 33

Size of downloaded dataset files:

222 MB

Size of the auto-converted Parquet files:

222 MB

Number of rows:

26,657