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International Journal of Electrical and Computer Engineering (IJECE)International Journal of Electrical and Computer Engineering (IJECE)

Untuk identifikasi sistem cluster sparse, diketahui bahwa cluster sparse improved (CS-IPAPA) lebih unggul daripada standar IPAPA. Namun, karena CS-IPAPA tidak mempertahankan faktor proporsional masa lalu, kinerjanya dapat lebih ditingkatkan. Dalam makalah ini, dimodifikasi CS-IPAPA dengan memanfaatkan elemen proporsional instan masa lalu berdasarkan kinerjanya. Kinerja algoritma cluster sparse improved proportionate affine projection (MCS-IPAPA) yang diusulkan diteliti dengan mendasarkan kondisi untuk stabilitas rata-rata. Berbagai setup simulasi menunjukkan bahwa algoritma yang diusulkan lebih unggul daripada versi IPAPA yang berbeda dalam hal kecepatan konvergensi, kesalahan normalisasi (NM), dan pelacakan, untuk berbagai jenis masukan seperti noise berwarna, noise putih, dan sinyal suara. Dengan menggabungkan faktor proporsional masa lalu, MCS-IPAPA yang diusulkan secara signifikan mengurangi kompleksitas komputasi untuk pesanan proyeksi yang lebih tinggi.

Dalam makalah ini, algoritma MCS-IPAPA yang diusulkan telah diperkenalkan untuk identifikasi sistem cluster sparse.Implikasi penelitian ini adalah dengan menambahkan memori ke CS-IPAPA, kompleksitas komputasinya secara signifikan berkurang.Peningkatan kinerja untuk CS-IPAPA dicapai dengan mempertimbangkan elemen proporsional terakhir p dalam memperkirakan bobot filter saat ini.Menambahkan memori menghasilkan pengurangan signifikan dalam jumlah perkalian dan peningkatan kinerja konvergensi dan stabilitas.Dibandingkan dengan CS-IPAPA, MCS-IPAPA menghemat pK-K perkalian, menunjukkan pengurangan yang signifikan dalam komputasi, terutama perkalian, untuk nilai p yang lebih tinggi.Kondisi untuk menjamin stabilitas rata-rata telah diturunkan.Simulasi eksperimental telah dilakukan untuk menganalisis bagaimana algoritma yang diusulkan lebih unggul daripada algoritma yang ada seperti IPAPA, MIPAPA, BS-IPAPA, dan CS-IPAPA dalam hal kecepatan konvergensi, NM, dan pelacakan.Dengan masukan noise putih, noise berwarna, dan sinyal suara, algoritma MCS-IPAPA secara signifikan meningkatkan NM dibandingkan CS-IPAPA dalam memperkirakan saluran cluster tunggal dan ganda.

Analisis dampak penggunaan memori tambahan akan dilakukan sebagai pekerjaan masa depan. Selain itu, pekerjaan masa depan akan berfokus pada optimasi strategi pemilihan memori dalam MCS-IPAPA. Penerapan MCS-IPAPA dalam skenario dunia nyata seperti komunikasi nirkabel dan pemrosesan sinyal biomedis akan dieksplorasi. Kelayakan implementasi hardware MCS-IPAPA pada FPGA/ASIC akan dipelajari.

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