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A self-organizing map consists of components called nodes or neurons. Associated with each node is a weight vector of the same dimension as the input data vectors and a position in the map space. The usual arrangement of nodes is a regular spacing in a hexagonal or rectangular grid. The self-organizing map describes a mapping from a higher dimensional input space to a lower dimensional map space. The procedure for placing a vector from data space onto the map is to find the node with the closest weight vector to the vector taken from data space and to assign the map coordinates of this node to our vector.

While it is typical to consider this type of network structure as related to feedforward networks where the nodes are visualized as being attached, this type of architecture is fundamentally different in arrangement and motivation.

Useful extensions include using toroidal grids where opposite edges are connected and using large numbers of nodes. It has been shown that while self-organizing maps with a small number of nodes behave in a way that is similar to K-means, larger self-organizing maps rearrange data in a way that is fundamentally topological in character.

It is also common to use the U-matrix. The U-matrix value of a particular node is the average distance between the node and its closest neighbors. In a rectangular grid for instance, we might consider the closest 4 or 8 nodes.

Large SOMs display properties which are emergent. Therefore, large maps are preferable to smaller ones. In maps consisting of thousands of nodes, it is possible to perform cluster operations on the map itself

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A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different than other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space.

This makes SOM useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map. ^[1]

Like most artificial neural networks, SOMs operate in two modes: training and mapping. Training builds the map using input examples. It is a competitive process, also called vector quantization. Mapping automatically classifies a new input vector.

For more informations please visit http://wikipedia.org

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The Travelling Salesman problem (TSP) is a problem in combinatorial optimization studied in operations research and theoretical computer science. Given a list of cities and their pairwise distances, the task is to find a shortest possible tour that visits each city exactly once.

The problem was first formulated as a mathematical problem in 1930 and is one of the most intensively studied problems in optimization. It is used as a benchmark for many optimization methods. Even though the problem is computationally difficult, a large number of heuristics and exact methods are known, so that some instances with tens of thousands of cities can be solved.

The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. Slightly modified, it appears as a sub-problem in many areas, such as genome sequencing. In these applications, the concept city represents, for example, customers, soldering points, or DNA fragments, and the concept distance represents travelling times or cost, or a similarity measure between DNA fragments. In many applications, additional constraints such as limited resources or time windows make the problem considerably harder.

In the theory of computational complexity, the decision version of TSP belongs to the class of NP-complete problems. Thus, it is assumed that there is no efficient algorithm for solving TSP problems. In other words, it is likely that the worst case running time for any algorithm for TSP increases exponentially with the number of cities, so even some instances with only hundreds of cities will take many CPU years to solve exactly.

For More information about Traveling Salesman Problem can be referense in httt://wikipedia.org

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Jaringan saraf tiruan (JST) (Bahasa Inggris: artificial neural network (ANN), atau juga disebut simulated neural network (SNN), atau umumnya hanya disebut neural network (NN)), adalah jaringan dari sekelompok unit pemroses kecil yang dimodelkan berdasarkan jaringan saraf manusia. JST merupakan sistem adaptif yang dapat merubah strukturnya untuk memecahkan masalah berdasarkan informasi eksternal maupun internal yang mengalir melalui jaringan tersebut.

Secara sederhana, JST adalah sebuah alat pemodelan data statistik non-linier. JST dapat digunakan untuk memodelkan hubungan yang kompleks antara input dan output untuk menemukan pola-pola pada data.

Sejarah JST

Saat ini bidang kecerdasan buatan dalam usahanya menirukan intelegensi manusia, belum mengadakan pendekatan dalam bentuk fisiknya melainkan dari sisi yang lain. Pertama-tama diadakan studi mengenai teori dasar mekanisme proses terjadinya intelegensi. Bidang ini disebut ‘Cognitive Science’. Dari teori dasar ini dibuatlah suatu model untuk disimulasikan pada komputer, dan dalam perkembangannya yang lebih lanjut dikenal berbagai sistem kecerdasan buatan yang salah satunya adalah jaringan saraf tiruan. Dibandingkan dengan bidang ilmu yang lain, jaringan saraf tiruan relatif masih baru. Sejumlah literatur menganggap bahwa konsep jaringan saraf tiruan bermula pada makalah Waffen McCulloch dan Walter Pitts pada tahun 1943. Dalam makalah tersebut mereka mencoba untuk memformulasikan model matematis sel-sel otak. Metode yang dikembangkan berdasarkan sistem saraf biologi ini, merupakan suatu langkah maju dalam industri komputer.

Konsep Dasar JST

Tidak ada dua otak manusia yang sama, setiap otak selalu berbeda. Beda dalam ketajaman, ukuran dan pengorganisasiannya. Salah satu cara untuk memahami bagaimana otak bekerja adalah dengan mengumpulkan informasi dari sebanyak mungkin scan otak manusia dan memetakannya. Hal tersebut merupakan upaya untuk menemukan cara kerja rata-rata otak manusia itu. Peta otak manusia diharapkan dapat menjelaskan misteri mengenai bagaimana otak mengendalikan setiap tindak tanduk manusia, mulai dari penggunaan bahasa hingga gerakan.

Walaupun demikian kepastian cara kerja otak manusia masih merupakan suatu misteri. Meski beberapa aspek dari prosesor yang menakjubkan ini telah diketahui tetapi itu tidaklah banyak. Beberapa aspek-aspek tersebut, yaitu :

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