Determining heating pipe temperature in greenhouse using proportional integral plus feedforward control and radial basic function neural-networks

Yu, Chao-gang; Wang, Jianping; Nourain, Jamal; Jia, Yingmin

doi:10.1631/jzus.2005.a0265

Cited by 4 publications

(1 citation statement)

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“…Perubahan kondisi iklim di luar sangat mempengaruhi suhu di dalam ruangan. Misalnya penurunan suhu di luar rumah kaca atau peningkatan angin akan menyebabkan penurunan suhu di dalam rumah kaca (Yu et al, 2005).…”

Section: Pendahuluanunclassified

Rancang Bangun Alat Pengendali Suhu dan Kelembaban Relatif pada Rumah Kaca dengan Informasi Berbasis Web

Zaida¹,

Ardiansah²,

Rizky³

2017

Teknotan

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Suhu dan kelembaban relatif (RH) adalah variabel penting dalam budidaya tanaman di rumah kaca, serta dapat dikendalikan melalui proses pendinginan evaporatif dengan menggunakan pompa fog cooling system, tetapi prosesnya masih dilakukan secara manual. Penelitian ini adalah merancang alat pengendali suhu dan RH secara otomatis, merancang sistem monitoring melalui website, dan dapat menurunkan suhu dan meningkatkan RH. Penelitian ini menggunakan metode rekayasa. Hasil penelitian membuktikan bahwa alat pengendali suhu dan RH dapat menyalakan pompa fog cooling system secara otomatis. Website dapat menampilkan data monitoring. Jika intensitas cahaya matahari di luar rumah kaca (So) lebih besar dari 800 Wm-2, suhu dapat diturunkan di bawah 35 oC dan RH dapat dinaikkan di atas 60%. Jika So 400 Wm-2 – 800 Wm-2 dan lebih kecil dari 400 Wm-2, suhu tidak dapat diturunkan dibawah 30 oC, tetapi RH dapat dinaikkan di atas 60%.Kata kunci: Fog Cooling System, Monitoring, Pengendalian Iklim Mikro, Rumah Kaca, Website

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Section: Pendahuluanunclassified

Rancang Bangun Alat Pengendali Suhu dan Kelembaban Relatif pada Rumah Kaca dengan Informasi Berbasis Web

Zaida¹,

Ardiansah²,

Rizky³

2017

Teknotan

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RBF Neural Network Based on Particle Swarm Optimization

Shao

Chen

Jiang

2010

Advances in Neural Networks - ISNN 2010

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Abstract. This paper develops a RBF neural network based on particle swarm optimization (PSO) algorithm. It is composed of a RBF neural network, whose parameters including clustering centers, variances of Radial Basis Function and weights are optimized by PSO algorithm. Therefore it has not only simplified the structure of RBF neural network, but also enhanced training speed and mapping accurate. The performance and effectiveness of the proposed method are evaluated by using function simulation and compared with RBF neural network. The result shows that the optimized RBF neural network has significant advantages inspect of fast convergence speed, good generalization ability and not easy to yield minimal local results.

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Performance evaluation of radial basis function networks based on tree seed algorithm

Muneeswaran

Rajasekaran

2016

2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT)

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Determining heating pipe temperature in greenhouse using proportional integral plus feedforward control and radial basic function neural-networks

Cited by 4 publications

References 0 publications

Rancang Bangun Alat Pengendali Suhu dan Kelembaban Relatif pada Rumah Kaca dengan Informasi Berbasis Web

Rancang Bangun Alat Pengendali Suhu dan Kelembaban Relatif pada Rumah Kaca dengan Informasi Berbasis Web

RBF Neural Network Based on Particle Swarm Optimization

Performance evaluation of radial basis function networks based on tree seed algorithm

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