On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments

Associative memories enjoy many interesting properties in terms of error correction capabilities, robustness to noise, storage capacity, and retrieval performance, and their usage spans over a large set of applications. In this letter, we investigate and extend tournament-based neural networks, originally proposed by Jiang, Gripon, Berrou, and Rabbat (2016), a novel sequence storage associative memory architecture with high memory efficiency and accurate sequence retrieval. We propose a more general method for learning the sequences, which we call feedback tournament-based neural networks. The retrieval process is also extended to both directions: forward and backward—in other words, any large-enough segment of a sequence can produce the whole sequence. Furthermore, two retrieval algorithms, cache-winner and explore-winner, are introduced to increase the retrieval performance. Through simulation results, we shed light on the strengths and weaknesses of each algorithm.

ANALISA PERANCANGAN KAPASITAS PENYIMPANAN OPTIMAL GUDANG BARANG JADI (STUDI KASUS: PT. SAGATRADE MURNI SAMARINDA)

Meningkatnya permintaan pasar akan suatu produk memaksa perusahaan untuk lebih meningkatkan kuantitas produksinya. Seiring meningkatnya kuantitas produksi, maka perusahaan memikirkan hal terpenting mengenai kebutuhan adanya tempat penyimpanan yang baik dan optimal. Penelitian ini memberikan gambaran bagaimana menganalisa kapasitas penyimpanan produk jadi pada perusahaan tersebut dengan menggunakan metode penyimpanan storage policy yaitu dedicated storage policy serta bagaimana menganalisa jalur lintas yang ada pada gudang. Hasil observasi lapangan memberikan hasil mengenai stok produk dari segi pengiriman dan pengeluarannya serta dimensi pengemasannya. Sistem penyimpanan mengacu pada metode dedicated storage policy yang bersifat part number sequence storage. Selanjutnya, untuk penempatan area penyimpanan seluruh varian yang ada seluas 224 m2, dan gambaran hasil analisa untuk penempatan area dan penyimpanan produk pada bulan November 2016 adalah 145,38 m2. Hasil ini menunjukkan bahwa penempatan penyimpanan dapat memberikan perluasan area penyimpanan seluas 78,62 m2. Sedangkan hasil jalur lintas dengan menentukan aisle space potential ratio menghasilkan presentase penambahan sebanyak 15 % dari jalur yang ada. Hasil ini merupakan analisa jalur lintas yang dilalui dua kendaran forklift.

Sequence Memory Based on Coherent Spin-Interaction Neural Networks

Sequence information processing, for instance, the sequence memory, plays an important role on many functions of brain. In the workings of the human brain, the steady-state period is alterable. However, in the existing sequence memory models using heteroassociations, the steady-state period cannot be changed in the sequence recall. In this work, a novel neural network model for sequence memory with controllable steady-state period based on coherent spininteraction is proposed. In the proposed model, neurons fire collectively in a phase-coherent manner, which lets a neuron group respond differently to different patterns and also lets different neuron groups respond differently to one pattern. The simulation results demonstrating the performance of the sequence memory are presented. By introducing a new coherent spin-interaction sequence memory model, the steady-state period can be controlled by dimension parameters and the overlap between the input pattern and the stored patterns. The sequence storage capacity is enlarged by coherent spin interaction compared with the existing sequence memory models. Furthermore, the sequence storage capacity has an exponential relationship to the dimension of the neural network.