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Abstract
A neural network is a form of artificial intelligence that has the ability to learn, grow, and adapt in a dynamic environment. Neural network began since 1890 because a great American psychologist named William James created the book Principles of Psycology. James was the first one publish a number of facts related to the structure and function of the brain. The history of neural network development is divided into 4 epochs, the Camelot era, the Depression, the Renaissance, and the Neoconnectiosm era. Neural networks used today are not 100 percent accurate. However, neural networks are still used because of better performance than alternative computing models. The use of neural network consists of pattern recognition, signal analysis, robotics, and expert systems. For risk analysis of the neural network, it is first performed using hazards and operability studies (HAZOPS). Determining the neural network requirements in a good way will help in determining its contribution to system hazards and validating the control or mitigation of any hazards. After completion of the first stage at HAZOPS and the second stage determines the requirements, the next stage is designing. Neural network underwent repeated design-train-test development. At the design stage, the hazard analysis should consider the design aspects of the development, which include neural network architecture, size, intended use, and so on. It will be continued at the implementation stage, test phase, installation and inspection phase, operation phase, and ends at the maintenance stage.
Highlights
Sejak awal, kecerdasan buatan telah difokuskan pada perbaikan di bidang ilmu komputer yang luas, dan telah memberikan kontribusi yang cukup besar untuk penelitian di berbagai bidang ilmiah dan teknis
A Neural network is a form of artificial intelligence that has the ability to learn
one publish a number of facts related to the structure and function of the brain
Summary
Kecerdasan buatan telah difokuskan pada perbaikan di bidang ilmu komputer yang luas, dan telah memberikan kontribusi yang cukup besar untuk penelitian di berbagai bidang ilmiah dan teknis. NASA IV & V Facility telah mengenali kebutuhan dan pentingnya teknologi neural network karena hal ini menjadi lebih layak untuk digunakan dalam aplikasi antariksa masa depan. Selama penelitian tiga tahun terakhir, NASA IV & V Facility telah memeriksa beberapa metode dan prosedur yang lebih menjanjikan untuk verifikasi dan validasi neural network dan sistem adaptif. Neural network bukan solusi sempurna, namun mampu menyebarkan metode dan prosedur untuk memverifikasi dan memvalidasi sistem yang sangat kompleks sehingga dapat digunakan dalam aplikasi yang kritis. Paper ini adalah alat yang sangat baik untuk mempersiapkan NASA IV & V dan juga praktisi verifikasi & validassi lainnya untuk memastikan sistem perangkat lunak neural network untuk misi NASA di masa depan. Panduan khusus untuk analisis resiko dan bahaya yang terkait dengan karakteristik khusus perangkat lunak neural network diperlukan, dan saat ini tidak tersedia.[3] dan [4] memberikan kerangka umum untuk penilaian probabilitas-probabilitas perangkat lunak. Tujuan penelitian yang diuraikan dalam paper ini adalah untuk menyediakan jalur penelitian yang disarankan untuk penilaian resiko sistem neural network
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