The probabilistic neural network architecture for high speed classification of remotely sensed imagery

In this paper we discuss a neural network architecture (the Probabilistic Neural Net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised non-parametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network which can be shown to take O(N6) time for training where N is the dimensionality of the input vector. In addition the PNN can be implemented in a feed forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the paper. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the backpropagation neural network (BPNN). The PNN is shown to be better than GMLC and not as good as the BPNN with regards to classification accuracy

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table

Heterobimetallic Complexes of Rhenium and Zinc: Potential Catalysts for Homogeneous Syngas Conversion

6-(Diphenylphosphino)-2,2′-bipyridine (PNN) coordinates to rhenium carbonyls in both κ^1(P) and κ^2(N,N) modes; in the former, the free bpy moiety readily binds to zinc alkyls and halides. [Re(κ^1(P)-PNN)(CO)_5][OTf] reacts with dialkylzinc reagents to form [Re(κ^1(P)-PNN·ZnR)(CO)_4(μ_(2-)C(O)R)][OTf] (R = Me, Et, Bn), in which an alkyl group has been transferred to a carbonyl carbon and the resulting monoalkyl Zn is bound both to the bpy nitrogens and the acyl oxygen. ZnCl_2 binds readily to the bpy group in Re(κ^1(P)-PNN)(CO)_4Me, and the resulting adduct undergoes facile migratory insertion, assisted by the Lewis acidic pendent Zn, to yield Re(κ^1(P)-PNN·ZnCl)(μ_(2-)Cl)(CO)_3(μ_(2-)C(O)Me), in which one of the chlorides occupies the sixth coordination site on Re. Migratory insertion is inhibited by THF or other ethers that can coordinate to ZnCl_2. Migratory insertion is also observed for Re(κ1(P)-PNN)(CO)_4(CH_2Ph) but not for Re(κ^1(P)-PNN)(CO)_4(CH_2OCH_3); coordination of the methoxy oxygen to Zn appears to block its ability to coordinate to the carbonyl oxygen and facilitate migratory insertion. Intramolecular Lewis acid promoted hydride transfer from [(dmpe)_2PtH][PF_6] to a carbonyl in [Re(κ^1(P)-PNN)(CO)_5][OTf] results in formation of a Re–formyl species; additional hydride transfer leads to a novel Re–Zn-bonded product along with some formal dehyde

Measurement of B(K -> pnn)

The experimental measurement of positive kaons decaying to a positive pion and a neutrino, anti-neutrino pair (pnn) is reviewed. Recent results from experiment E787 at BNL are presented: with data from 1995--97 the branching ratio has been measured to be B(pnn) = (1.5^{+3.4}_{-1.2}) \times10^{-10}. The future prospects for additional data in this mode are examined.Comment: 5 pages, 2 figures, LaTeX, invited talk at the 7th Conference on the Intersections of Particle and Nuclear Physics; Quebec City, Canada, May 22-28, 200

Pemilihan Parameter Smoothing pada Probabilistic Neural Network dengan Menggunakan Particle Swarm Optimization untuk Pendeteksian Teks pada Citra

Teks sering dijumpai di berbagai tempat seperti nama jalan, nama toko, spanduk, penunjuk jalan, peringatan, dan lain sebagainya. Deteksi teks terbagi menjadi tiga pendekatan yaitu pendekatan tekstur, pendekatan edge, dan pendekatan Connected Component. Pendekatan tekstur dapat mendeteksi teks dengan baik, namun membutuhkan data training yang banyak. Probabilistic Neural Netwok (PNN) dapat mengatasi permasalahan tersebut. Namun PNN memiliki permasalahan dalam menentukan nilai parameter smoothing yang biasanya dilakukan secara trial and error. Particle Swarm Optimization (PSO) merupakan algoritma optimasi yang dapat menangani permasalahan pada PNN. Pada penelitian ini, PNN digunakan pada pendekatan tekstur guna menangani permasalahan pada pendekatan tekstur, yaitu banyaknya data training yang dibutuhkan. Selain itu, digunakan PSO untuk menentukan parameter smoothing pada PNN agar akurasi yang dihasilkan PNN-PSO lebih baik dari PNN tradisional. Hasil eksperimen menunjukkan PNN dapat mendeteksi teks dengan akurasi 75,42% hanya dengan mengunakan 300 data training, dan menghasilkan 77,75% dengan menggunakan 1500 data training. Sedangkan PNN-PSO dapat menghasilkan akurasi 76,91% dengan menggunakan 300 data training dan 77,89% dengan menggunakan 1500 data training. Maka dapat disimpulkan bahwa PNN dapat mendeteksi teks dengan baik walaupun data training yang digunakan sedikit dan dapat mengatasi permasalahan pada pendekatan tekstur. Sedangkan, PSO dapat menentukan nilai parameter smoothing pada PNN dan menghasilkan akurasi yang lebih baik dari PNN tradisional, yaitu dengan peningkatan akurasi sekitar 0,1% hingga 1,5%. Selain itu, penggunaan PSO pada PNN dapat digunakan dalam menentukan nilai parameter smoothing secara otomatis pada dataset yang berbeda