Rangkaian Neural Perseptron Berbilang Lapisan Hibrid Berkelompok Untuk Pengelasan Corak Yang Lebih Balk - Wan Mamat, Wan Mohd Fahmi (2009)

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Rangkaian neural berdasarkan konsep Perseptron dan Fungsi Asas Jejarian (RBF) sering digunakan sebagai penge1as corak pintar. Namun, rangkaian neural berasaskan konsep Perseptron mempunyai kelemahan seperti kadar penumpuan yang perlahan, proses pencarian pemberat optimum rangkaian yang sering terperangkap di dalam minima setempat dan sensitif kepada nilai p_embolehubah awalan. Manakala, rangkaian neural berasaskan konsep RBF berhadapan dengan tiga masalah tipikal seperti fenomena pus at mati, kehadiran pusat bertindih dan pusat yang terperangkap di dalam minima setempat. Maka, penyelidikan ini mencadangkan satu seni bina rangkaian. neural baru yang dinamakan Perseptron Berbilang Lapisan Hibrid Berkelompok atau Clustered-HMLP. Seni bina rangkaian Clustered-HMLP adalah berasaskan seni bina rangkaian Perseptron Berbilang Lapisan Hibrid yang diubahsuai melalui penambahan satu lapisan tambahan yang dinamakan lapisan pusat. Perseptron based and Radial Basis Function (RBF) neural networks are commonly used for pattern classification. However, their performances are limited to several weaknesses. For the Perseptron based neural networks, their training procedures are often trapped at a local optimum with slow convergence rate and sensitive to initial parameter values. Whereas, three typical problems for the RBF network are dead centers, centers redundancy and trapped centers in local minima. Thus, this study introduces a new neural network architecture called Clustered Hybrid Multilayered Perceptron or ClusteredHMLP. In this work, the Hybrid Multilayered Perceptron network architecture has been modified by introducing an additional layer called cluster layer to form the proposed neural network. The cluster layer concept is adopted from the RBF network architecture.